Mapping storage of data in a dispersed storage network

ABSTRACT

A method begins by a dispersed storage (DS) processing module receiving data for storage in a dispersed storage network (DSN) memory and ascertaining dispersed storage error encoding parameters for encoding the data. The method continues with the DS processing module ascertaining storage units of the DSN memory for the storing an encoded version of the data and ascertaining a storage mapping that maps encoded data slices to storage units for storing the encoded version of the data. The method continues with the DS processing module encoding the data in accordance with the dispersed storage error encoding parameters to produce sets of encoded data slices. The method continues with the DS processing module generating a plurality of write requests for storing, in accordance with the storage mapping, encoded data slices of the sets of encoded data slices in a pattern across the storage units.

CROSS REFERENCE TO RELATED PATENTS

This patent application is claiming priority under 35 USC §119(e) to aprovisionally filed patent application entitled UTILIZING A HIERARCHICALREGION HEADER OBJECT STRUCTURE FOR DATA STORAGE (Attorney Docket No.CS01264) having a provisional filing date of Jan. 4, 2013, and aprovisional Ser. No. 61/748,916, which is incorporated herein byreference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

NOT APPLICABLE

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

NOT APPLICABLE

BACKGROUND OF THE INVENTION

1. Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersed storage of data and distributed taskprocessing of data.

2. Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a diagram of an example of a distributed storage and taskprocessing in accordance with the present invention;

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 5 is a logic diagram of an example of a method for outbound DSTprocessing in accordance with the present invention;

FIG. 6 is a schematic block diagram of an embodiment of a dispersederror encoding in accordance with the present invention;

FIG. 7 is a diagram of an example of a segment processing of thedispersed error encoding in accordance with the present invention;

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding in accordance with thepresent invention;

FIG. 9 is a diagram of an example of grouping selection processing ofthe outbound DST processing in accordance with the present invention;

FIG. 10 is a diagram of an example of converting data into slice groupsin accordance with the present invention;

FIG. 11 is a schematic block diagram of an embodiment of a DST executionunit in accordance with the present invention;

FIG. 12 is a schematic block diagram of an example of operation of a DSTexecution unit in accordance with the present invention;

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 14 is a logic diagram of an example of a method for inbound DSTprocessing in accordance with the present invention;

FIG. 15 is a diagram of an example of de-grouping selection processingof the inbound DST processing in accordance with the present invention;

FIG. 16 is a schematic block diagram of an embodiment of a dispersederror decoding in accordance with the present invention;

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of the dispersed error decoding in accordance with thepresent invention;

FIG. 18 is a diagram of an example of a de-segment processing of thedispersed error decoding in accordance with the present invention;

FIG. 19 is a diagram of an example of converting slice groups into datain accordance with the present invention;

FIG. 20 is a diagram of an example of a distributed storage within thedistributed computing system in accordance with the present invention;

FIG. 21 is a schematic block diagram of an example of operation ofoutbound distributed storage and/or task (DST) processing for storingdata in accordance with the present invention;

FIG. 22 is a schematic block diagram of an example of a dispersed errorencoding for the example of FIG. 21 in accordance with the presentinvention;

FIG. 23 is a diagram of an example of converting data into pillar slicegroups for storage in accordance with the present invention;

FIG. 24 is a schematic block diagram of an example of a storageoperation of a DST execution unit in accordance with the presentinvention;

FIG. 25 is a schematic block diagram of an example of operation ofinbound distributed storage and/or task (DST) processing for retrievingdispersed error encoded data in accordance with the present invention;

FIG. 26 is a schematic block diagram of an example of a dispersed errordecoding for the example of FIG. 25 in accordance with the presentinvention;

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing a plurality ofdata and a plurality of task codes in accordance with the presentinvention;

FIG. 28 is a schematic block diagram of an example of the distributedcomputing system performing tasks on stored data in accordance with thepresent invention;

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module facilitating the example of FIG. 28 in accordancewith the present invention;

FIG. 30 is a diagram of a specific example of the distributed computingsystem performing tasks on stored data in accordance with the presentinvention;

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30 in accordance with the presentinvention;

FIG. 32 is a diagram of an example of DST allocation information for theexample of FIG. 30 in accordance with the present invention;

FIGS. 33-38 are schematic block diagrams of the DSTN module performingthe example of FIG. 30 in accordance with the present invention;

FIG. 39 is a diagram of an example of combining result information intofinal results for the example of FIG. 30 in accordance with the presentinvention;

FIG. 40 is a diagram of an embodiment of a structure of a region headerobject in accordance with the present invention;

FIG. 41 is a flowchart illustrating an example of reading a data objectin accordance with the present invention;

FIG. 42 is a flowchart illustrating an example of writing a data objectin accordance with the present invention;

FIG. 43A is a diagram of an embodiment of a structure of a hierarchicalregion header object in accordance with the present invention;

FIG. 43B is a flowchart illustrating another example of reading a dataobject in accordance with the present invention;

FIG. 44 is a flowchart illustrating another example of writing a dataobject in accordance with the present invention;

FIG. 45A is a schematic block diagram of an embodiment of a dispersedstorage system in accordance with the present invention;

FIG. 45B is a flowchart illustrating an example of renaming a namedobject in accordance with the present invention;

FIG. 46A is a schematic block diagram of another embodiment of adispersed storage system in accordance with the present invention;

FIG. 46B is a flowchart illustrating another example of writing a dataobject in accordance with the present invention;

FIGS. 47A, 47G, and 47H are schematic block diagrams of an embodiment ofa dispersed storage network (DSN) in accordance with the presentinvention;

FIGS. 47B-C are diagrams illustrating examples of a slice selectiontable in accordance with the present invention;

FIGS. 47D-F are diagrams illustrating examples of a storage unitselection table in accordance with the present invention;

FIG. 47I is a flowchart illustrating an example of storing data inaccordance with the present invention;

FIG. 48 is a flowchart illustrating another example of storing data inaccordance with the present invention;

FIG. 49 is a flowchart illustrating another example of storing data inaccordance with the present invention;

FIG. 50 is a flowchart illustrating another example of storing data inaccordance with the present invention;

FIG. 51A is a diagram illustrating an example of a virtual dispersedstorage network (DSN) address to physical location table in accordancewith the present invention; and

FIG. 51B is a flowchart illustrating an example of assigning dispersedstorage network (DSN) address ranges in accordance with the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system 10 that includes a user device 12 and/or a user device14, a distributed storage and/or task (DST) processing unit 16, adistributed storage and/or task network (DSTN) managing unit 18, a DSTintegrity processing unit 20, and a distributed storage and/or tasknetwork (DSTN) module 22. The components of the distributed computingsystem 10 are coupled via a network 24, which may include one or morewireless and/or wire lined communication systems; one or more privateintranet systems and/or public internet systems; and/or one or morelocal area networks (LAN) and/or wide area networks (WAN).

The DSTN module 22 includes a plurality of distributed storage and/ortask (DST) execution units 36 that may be located at geographicallydifferent sites (e.g., one in Chicago, one in Milwaukee, etc.). Each ofthe DST execution units is operable to store dispersed error encodeddata and/or to execute, in a distributed manner, one or more tasks ondata. The tasks may be a simple function (e.g., a mathematical function,a logic function, an identify function, a find function, a search enginefunction, a replace function, etc.), a complex function (e.g.,compression, human and/or computer language translation, text-to-voiceconversion, voice-to-text conversion, etc.), multiple simple and/orcomplex functions, one or more algorithms, one or more applications,etc.

Each of the user devices 12-14, the DST processing unit 16, the DSTNmanaging unit 18, and the DST integrity processing unit 20 include acomputing core 26 and may be a portable computing device and/or a fixedcomputing device. A portable computing device may be a social networkingdevice, a gaming device, a cell phone, a smart phone, a personal digitalassistant, a digital music player, a digital video player, a laptopcomputer, a handheld computer, a tablet, a video game controller, and/orany other portable device that includes a computing core. A fixedcomputing device may be a personal computer (PC), a computer server, acable set-top box, a satellite receiver, a television set, a printer, afax machine, home entertainment equipment, a video game console, and/orany type of home or office computing equipment. User device 12 and DSTprocessing unit 16 are configured to include a DST client module 34.

With respect to interfaces, each interface 30, 32, and 33 includessoftware and/or hardware to support one or more communication links viathe network 24 indirectly and/or directly. For example, interfaces 30support a communication link (e.g., wired, wireless, direct, via a LAN,via the network 24, etc.) between user device 14 and the DST processingunit 16. As another example, interface 32 supports communication links(e.g., a wired connection, a wireless connection, a LAN connection,and/or any other type of connection to/from the network 24) between userdevice 12 and the DSTN module 22 and between the DST processing unit 16and the DSTN module 22. As yet another example, interface 33 supports acommunication link for each of the DSTN managing unit 18 and DSTintegrity processing unit 20 to the network 24.

The distributed computing system 10 is operable to support dispersedstorage (DS) error encoded data storage and retrieval, to supportdistributed task processing on received data, and/or to supportdistributed task processing on stored data. In general and with respectto DS error encoded data storage and retrieval, the distributedcomputing system 10 supports three primary operations: storagemanagement, data storage and retrieval (an example of which will bediscussed with reference to FIGS. 20-26), and data storage integrityverification. In accordance with these three primary functions, data canbe encoded, distributedly stored in physically different locations, andsubsequently retrieved in a reliable and secure manner. Such a system istolerant of a significant number of failures (e.g., up to a failurelevel, which may be greater than or equal to a pillar width minus adecode threshold minus one) that may result from individual storagedevice failures and/or network equipment failures without loss of dataand without the need for a redundant or backup copy. Further, the systemallows the data to be stored for an indefinite period of time withoutdata loss and does so in a secure manner (e.g., the system is veryresistant to attempts at hacking the data).

The second primary function (i.e., distributed data storage andretrieval) begins and ends with a user device 12-14. For instance, if asecond type of user device 14 has data 40 to store in the DSTN module22, it sends the data 40 to the DST processing unit 16 via its interface30. The interface 30 functions to mimic a conventional operating system(OS) file system interface (e.g., network file system (NFS), flash filesystem (FFS), disk file system (DFS), file transfer protocol (FTP),web-based distributed authoring and versioning (WebDAV), etc.) and/or ablock memory interface (e.g., small computer system interface (SCSI),internet small computer system interface (iSCSI), etc.). In addition,the interface 30 may attach a user identification code (ID) to the data40.

To support storage management, the DSTN managing unit 18 performs DSmanagement services. One such DS management service includes the DSTNmanaging unit 18 establishing distributed data storage parameters (e.g.,vault creation, distributed storage parameters, security parameters,billing information, user profile information, etc.) for a user device12-14 individually or as part of a group of user devices. For example,the DSTN managing unit 18 coordinates creation of a vault (e.g., avirtual memory block) within memory of the DSTN module 22 for a userdevice, a group of devices, or for public access and establishes pervault dispersed storage (DS) error encoding parameters for a vault. TheDSTN managing unit 18 may facilitate storage of DS error encodingparameters for each vault of a plurality of vaults by updating registryinformation for the distributed computing system 10. The facilitatingincludes storing updated registry information in one or more of the DSTNmodule 22, the user device 12, the DST processing unit 16, and the DSTintegrity processing unit 20.

The DS error encoding parameters (e.g., or dispersed storage errorcoding parameters) include data segmenting information (e.g., how manysegments data (e.g., a file, a group of files, a data block, etc.) isdivided into), segment security information (e.g., per segmentencryption, compression, integrity checksum, etc.), error codinginformation (e.g., pillar width, decode threshold, read threshold, writethreshold, etc.), slicing information (e.g., the number of encoded dataslices that will be created for each data segment); and slice securityinformation (e.g., per encoded data slice encryption, compression,integrity checksum, etc.).

The DSTN managing module 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSTN module 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSTN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a private vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

Another DS management service includes the DSTN managing unit 18performing network operations, network administration, and/or networkmaintenance. Network operations includes authenticating user dataallocation requests (e.g., read and/or write requests), managingcreation of vaults, establishing authentication credentials for userdevices, adding/deleting components (e.g., user devices, DST executionunits, and/or DST processing units) from the distributed computingsystem 10, and/or establishing authentication credentials for DSTexecution units 36. Network administration includes monitoring devicesand/or units for failures, maintaining vault information, determiningdevice and/or unit activation status, determining device and/or unitloading, and/or determining any other system level operation thataffects the performance level of the system 10. Network maintenanceincludes facilitating replacing, upgrading, repairing, and/or expandinga device and/or unit of the system 10.

To support data storage integrity verification within the distributedcomputing system 10, the DST integrity processing unit 20 performsrebuilding of ‘bad’ or missing encoded data slices.

At a high level, the DST integrity processing unit 20 performsrebuilding by periodically attempting to retrieve/list encoded dataslices, and/or slice names of the encoded data slices, from the DSTNmodule 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in memory of the DSTN module 22.Note that the DST integrity processing unit 20 may be a separate unit asshown, it may be included in the DSTN module 22, it may be included inthe DST processing unit 16, and/or distributed among the DST executionunits 36.

To support distributed task processing on received data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task processing) management and DST execution on received data(an example of which will be discussed with reference to FIGS. 3-19).With respect to the storage portion of the DST management, the DSTNmanaging unit 18 functions as previously described. With respect to thetasking processing of the DST management, the DSTN managing unit 18performs distributed task processing (DTP) management services. One suchDTP management service includes the DSTN managing unit 18 establishingDTP parameters (e.g., user-vault affiliation information, billinginformation, user-task information, etc.) for a user device 12-14individually or as part of a group of user devices.

Another DTP management service includes the DSTN managing unit 18performing DTP network operations, network administration (which isessentially the same as described above), and/or network maintenance(which is essentially the same as described above). Network operationsincludes, but is not limited to, authenticating user task processingrequests (e.g., valid request, valid user, etc.), authenticating resultsand/or partial results, establishing DTP authentication credentials foruser devices, adding/deleting components (e.g., user devices, DSTexecution units, and/or DST processing units) from the distributedcomputing system, and/or establishing DTP authentication credentials forDST execution units.

To support distributed task processing on stored data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task) management and DST execution on stored data. With respectto the DST execution on stored data, if the second type of user device14 has a task request 38 for execution by the DSTN module 22, it sendsthe task request 38 to the DST processing unit 16 via its interface 30.An example of DST execution on stored data will be discussed in greaterdetail with reference to FIGS. 27-39. With respect to the DSTmanagement, it is substantially similar to the DST management to supportdistributed task processing on received data.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (TO)controller 56, a peripheral component interconnect (PCI) interface 58,an 10 interface module 60, at least one 10 device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSTN interface module 76.

The DSTN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). TheDSTN interface module 76 and/or the network interface module 70 mayfunction as the interface 30 of the user device 14 of FIG. 1. Furthernote that the IO device interface module 62 and/or the memory interfacemodules may be collectively or individually referred to as IO ports.

FIG. 3 is a diagram of an example of the distributed computing systemperforming a distributed storage and task processing operation. Thedistributed computing system includes a DST (distributed storage and/ortask) client module 34 (which may be in user device 14 and/or in DSTprocessing unit 16 of FIG. 1), a network 24, a plurality of DSTexecution units 1-n that includes two or more DST execution units 36 ofFIG. 1 (which form at least a portion of DSTN module 22 of FIG. 1), aDST managing module (not shown), and a DST integrity verification module(not shown). The DST client module 34 includes an outbound DSTprocessing section 80 and an inbound DST processing section 82. Each ofthe DST execution units 1-n includes a controller 86, a processingmodule 84, memory 88, a DT (distributed task) execution module 90, and aDST client module 34.

In an example of operation, the DST client module 34 receives data 92and one or more tasks 94 to be performed upon the data 92. The data 92may be of any size and of any content, where, due to the size (e.g.,greater than a few Terra-Bytes), the content (e.g., secure data, etc.),and/or task(s) (e.g., MIPS intensive), distributed processing of thetask(s) on the data is desired. For example, the data 92 may be one ormore digital books, a copy of a company's emails, a large-scale Internetsearch, a video security file, one or more entertainment video files(e.g., television programs, movies, etc.), data files, and/or any otherlarge amount of data (e.g., greater than a few Terra-Bytes).

Within the DST client module 34, the outbound DST processing section 80receives the data 92 and the task(s) 94. The outbound DST processingsection 80 processes the data 92 to produce slice groupings 96. As anexample of such processing, the outbound DST processing section 80partitions the data 92 into a plurality of data partitions. For eachdata partition, the outbound DST processing section 80 dispersed storage(DS) error encodes the data partition to produce encoded data slices andgroups the encoded data slices into a slice grouping 96. In addition,the outbound DST processing section 80 partitions the task 94 intopartial tasks 98, where the number of partial tasks 98 may correspond tothe number of slice groupings 96.

The outbound DST processing section 80 then sends, via the network 24,the slice groupings 96 and the partial tasks 98 to the DST executionunits 1-n of the DSTN module 22 of FIG. 1. For example, the outbound DSTprocessing section 80 sends slice group 1 and partial task 1 to DSTexecution unit 1. As another example, the outbound DST processingsection 80 sends slice group #n and partial task #n to DST executionunit #n.

Each DST execution unit performs its partial task 98 upon its slicegroup 96 to produce partial results 102. For example, DST execution unit#1 performs partial task #1 on slice group #1 to produce a partialresult #1, for results. As a more specific example, slice group #1corresponds to a data partition of a series of digital books and thepartial task #1 corresponds to searching for specific phrases, recordingwhere the phrase is found, and establishing a phrase count. In this morespecific example, the partial result #1 includes information as to wherethe phrase was found and includes the phrase count.

Upon completion of generating their respective partial results 102, theDST execution units send, via the network 24, their partial results 102to the inbound DST processing section 82 of the DST client module 34.The inbound DST processing section 82 processes the received partialresults 102 to produce a result 104. Continuing with the specificexample of the preceding paragraph, the inbound DST processing section82 combines the phrase count from each of the DST execution units 36 toproduce a total phrase count. In addition, the inbound DST processingsection 82 combines the ‘where the phrase was found’ information fromeach of the DST execution units 36 within their respective datapartitions to produce ‘where the phrase was found’ information for theseries of digital books.

In another example of operation, the DST client module 34 requestsretrieval of stored data within the memory of the DST execution units 36(e.g., memory of the DSTN module). In this example, the task 94 isretrieve data stored in the memory of the DSTN module. Accordingly, theoutbound DST processing section 80 converts the task 94 into a pluralityof partial tasks 98 and sends the partial tasks 98 to the respective DSTexecution units 1-n.

In response to the partial task 98 of retrieving stored data, a DSTexecution unit 36 identifies the corresponding encoded data slices 100and retrieves them. For example, DST execution unit #1 receives partialtask #1 and retrieves, in response thereto, retrieved slices #1. The DSTexecution units 36 send their respective retrieved slices 100 to theinbound DST processing section 82 via the network 24.

The inbound DST processing section 82 converts the retrieved slices 100into data 92. For example, the inbound DST processing section 82de-groups the retrieved slices 100 to produce encoded slices per datapartition. The inbound DST processing section 82 then DS error decodesthe encoded slices per data partition to produce data partitions. Theinbound DST processing section 82 de-partitions the data partitions torecapture the data 92.

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module 34 FIG. 1 coupled to a DSTN module 22 of a FIG. 1 (e.g., aplurality of n DST execution units 36) via a network 24. The outboundDST processing section 80 includes a data partitioning module 110, adispersed storage (DS) error encoding module 112, a grouping selectormodule 114, a control module 116, and a distributed task control module118.

In an example of operation, the data partitioning module 110 partitionsdata 92 into a plurality of data partitions 120. The number ofpartitions and the size of the partitions may be selected by the controlmodule 116 via control 160 based on the data 92 (e.g., its size, itscontent, etc.), a corresponding task 94 to be performed (e.g., simple,complex, single step, multiple steps, etc.), DS encoding parameters(e.g., pillar width, decode threshold, write threshold, segment securityparameters, slice security parameters, etc.), capabilities of the DSTexecution units 36 (e.g., processing resources, availability ofprocessing recourses, etc.), and/or as may be inputted by a user, systemadministrator, or other operator (human or automated). For example, thedata partitioning module 110 partitions the data 92 (e.g., 100Terra-Bytes) into 100,000 data segments, each being 1 Giga-Byte in size.Alternatively, the data partitioning module 110 partitions the data 92into a plurality of data segments, where some of data segments are of adifferent size, are of the same size, or a combination thereof.

The DS error encoding module 112 receives the data partitions 120 in aserial manner, a parallel manner, and/or a combination thereof. For eachdata partition 120, the DS error encoding module 112 DS error encodesthe data partition 120 in accordance with control information 160 fromthe control module 116 to produce encoded data slices 122. The DS errorencoding includes segmenting the data partition into data segments,segment security processing (e.g., encryption, compression,watermarking, integrity check (e.g., CRC), etc.), error encoding,slicing, and/or per slice security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC), etc.). Thecontrol information 160 indicates which steps of the DS error encodingare active for a given data partition and, for active steps, indicatesthe parameters for the step. For example, the control information 160indicates that the error encoding is active and includes error encodingparameters (e.g., pillar width, decode threshold, write threshold, readthreshold, type of error encoding, etc.).

The group selecting module 114 groups the encoded slices 122 of a datapartition into a set of slice groupings 96. The number of slicegroupings corresponds to the number of DST execution units 36 identifiedfor a particular task 94. For example, if five DST execution units 36are identified for the particular task 94, the group selecting modulegroups the encoded slices 122 of a data partition into five slicegroupings 96. The group selecting module 114 outputs the slice groupings96 to the corresponding DST execution units 36 via the network 24.

The distributed task control module 118 receives the task 94 andconverts the task 94 into a set of partial tasks 98. For example, thedistributed task control module 118 receives a task to find where in thedata (e.g., a series of books) a phrase occurs and a total count of thephrase usage in the data. In this example, the distributed task controlmodule 118 replicates the task 94 for each DST execution unit 36 toproduce the partial tasks 98. In another example, the distributed taskcontrol module 118 receives a task to find where in the data a firstphrase occurs, wherein in the data a second phrase occurs, and a totalcount for each phrase usage in the data. In this example, thedistributed task control module 118 generates a first set of partialtasks 98 for finding and counting the first phase and a second set ofpartial tasks for finding and counting the second phrase. Thedistributed task control module 118 sends respective first and/or secondpartial tasks 98 to each DST execution unit 36.

FIG. 5 is a logic diagram of an example of a method for outbounddistributed storage and task (DST) processing that begins at step 126where a DST client module receives data and one or more correspondingtasks. The method continues at step 128 where the DST client moduledetermines a number of DST units to support the task for one or moredata partitions. For example, the DST client module may determine thenumber of DST units to support the task based on the size of the data,the requested task, the content of the data, a predetermined number(e.g., user indicated, system administrator determined, etc.), availableDST units, capability of the DST units, and/or any other factorregarding distributed task processing of the data. The DST client modulemay select the same DST units for each data partition, may selectdifferent DST units for the data partitions, or a combination thereof.

The method continues at step 130 where the DST client module determinesprocessing parameters of the data based on the number of DST unitsselected for distributed task processing. The processing parametersinclude data partitioning information, DS encoding parameters, and/orslice grouping information. The data partitioning information includes anumber of data partitions, size of each data partition, and/ororganization of the data partitions (e.g., number of data blocks in apartition, the size of the data blocks, and arrangement of the datablocks). The DS encoding parameters include segmenting information,segment security information, error encoding information (e.g.,dispersed storage error encoding function parameters including one ormore of pillar width, decode threshold, write threshold, read threshold,generator matrix), slicing information, and/or per slice securityinformation. The slice grouping information includes informationregarding how to arrange the encoded data slices into groups for theselected DST units. As a specific example, if the DST client moduledetermines that five DST units are needed to support the task, then itdetermines that the error encoding parameters include a pillar width offive and a decode threshold of three.

The method continues at step 132 where the DST client module determinestask partitioning information (e.g., how to partition the tasks) basedon the selected DST units and data processing parameters. The dataprocessing parameters include the processing parameters and DST unitcapability information. The DST unit capability information includes thenumber of DT (distributed task) execution units, execution capabilitiesof each DT execution unit (e.g., MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or the other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.)), and/or any information germane toexecuting one or more tasks.

The method continues at step 134 where the DST client module processesthe data in accordance with the processing parameters to produce slicegroupings. The method continues at step 136 where the DST client modulepartitions the task based on the task partitioning information toproduce a set of partial tasks. The method continues at step 138 wherethe DST client module sends the slice groupings and the correspondingpartial tasks to respective DST units.

FIG. 6 is a schematic block diagram of an embodiment of the dispersedstorage (DS) error encoding module 112 of an outbound distributedstorage and task (DST) processing section. The DS error encoding module112 includes a segment processing module 142, a segment securityprocessing module 144, an error encoding module 146, a slicing module148, and a per slice security processing module 150. Each of thesemodules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receives adata partition 120 from a data partitioning module and receivessegmenting information as the control information 160 from the controlmodule 116. The segmenting information indicates how the segmentprocessing module 142 is to segment the data partition 120. For example,the segmenting information indicates how many rows to segment the databased on a decode threshold of an error encoding scheme, indicates howmany columns to segment the data into based on a number and size of datablocks within the data partition 120, and indicates how many columns toinclude in a data segment 152. The segment processing module 142segments the data 120 into data segments 152 in accordance with thesegmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., cyclic redundancy check(CRC), etc.), and/or any other type of digital security. For example,when the segment security processing module 144 is enabled, it maycompress a data segment 152, encrypt the compressed data segment, andgenerate a CRC value for the encrypted data segment to produce a securedata segment 154. When the segment security processing module 144 is notenabled, it passes the data segments 152 to the error encoding module146 or is bypassed such that the data segments 152 are provided to theerror encoding module 146.

The error encoding module 146 encodes the secure data segments 154 inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters (e.g., also referred to as dispersed storage errorcoding parameters) include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Salomon basedalgorithm, an online coding algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction encodingparameters identify a specific error correction encoding scheme,specifies a pillar width of five, and specifies a decode threshold ofthree. From these parameters, the error encoding module 146 encodes adata segment 154 to produce an encoded data segment 156.

The slicing module 148 slices the encoded data segment 156 in accordancewith the pillar width of the error correction encoding parametersreceived as control information 160. For example, if the pillar width isfive, the slicing module 148 slices an encoded data segment 156 into aset of five encoded data slices. As such, for a plurality of datasegments 156 for a given data partition, the slicing module outputs aplurality of sets of encoded data slices 158.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice 158 based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it compresses anencoded data slice 158, encrypts the compressed encoded data slice, andgenerates a CRC value for the encrypted encoded data slice to produce asecure encoded data slice 122. When the per slice security processingmodule 150 is not enabled, it passes the encoded data slices 158 or isbypassed such that the encoded data slices 158 are the output of the DSerror encoding module 112. Note that the control module 116 may beomitted and each module stores its own parameters.

FIG. 7 is a diagram of an example of a segment processing of a dispersedstorage (DS) error encoding module. In this example, a segmentprocessing module 142 receives a data partition 120 that includes 45data blocks (e.g., d1-d45), receives segmenting information (i.e.,control information 160) from a control module, and segments the datapartition 120 in accordance with the control information 160 to producedata segments 152. Each data block may be of the same size as other datablocks or of a different size. In addition, the size of each data blockmay be a few bytes to megabytes of data. As previously mentioned, thesegmenting information indicates how many rows to segment the datapartition into, indicates how many columns to segment the data partitioninto, and indicates how many columns to include in a data segment.

In this example, the decode threshold of the error encoding scheme isthree; as such the number of rows to divide the data partition into isthree. The number of columns for each row is set to 15, which is basedon the number and size of data blocks. The data blocks of the datapartition are arranged in rows and columns in a sequential order (i.e.,the first row includes the first 15 data blocks; the second row includesthe second 15 data blocks; and the third row includes the last 15 datablocks).

With the data blocks arranged into the desired sequential order, theyare divided into data segments based on the segmenting information. Inthis example, the data partition is divided into 8 data segments; thefirst 7 include 2 columns of three rows and the last includes 1 columnof three rows. Note that the first row of the 8 data segments is insequential order of the first 15 data blocks; the second row of the 8data segments in sequential order of the second 15 data blocks; and thethird row of the 8 data segments in sequential order of the last 15 datablocks. Note that the number of data blocks, the grouping of the datablocks into segments, and size of the data blocks may vary toaccommodate the desired distributed task processing function.

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding processing the data segmentsof FIG. 7. In this example, data segment 1 includes 3 rows with each rowbeing treated as one word for encoding. As such, data segment 1 includesthree words for encoding: word 1 including data blocks d1 and d2, word 2including data blocks d16 and d17, and word 3 including data blocks d31and d32. Each of data segments 2-7 includes three words where each wordincludes two data blocks. Data segment 8 includes three words where eachword includes a single data block (e.g., d15, d30, and d45).

In operation, an error encoding module 146 and a slicing module 148convert each data segment into a set of encoded data slices inaccordance with error correction encoding parameters as controlinformation 160. More specifically, when the error correction encodingparameters indicate a unity matrix Reed-Solomon based encodingalgorithm, 5 pillars, and decode threshold of 3, the first three encodeddata slices of the set of encoded data slices for a data segment aresubstantially similar to the corresponding word of the data segment. Forinstance, when the unity matrix Reed-Solomon based encoding algorithm isapplied to data segment 1, the content of the first encoded data slice(DS1_d1&2) of the first set of encoded data slices (e.g., correspondingto data segment 1) is substantially similar to content of the first word(e.g., d1 & d2); the content of the second encoded data slice(DS1_d16&17) of the first set of encoded data slices is substantiallysimilar to content of the second word (e.g., d16 & d17); and the contentof the third encoded data slice (DS 1_d31&32) of the first set ofencoded data slices is substantially similar to content of the thirdword (e.g., d31 & d32).

The content of the fourth and fifth encoded data slices (e.g., ES1_(—)1and ES1_(—)2) of the first set of encoded data slices include errorcorrection data based on the first-third words of the first datasegment. With such an encoding and slicing scheme, retrieving any threeof the five encoded data slices allows the data segment to be accuratelyreconstructed.

The encoding and slices of data segments 2-7 yield sets of encoded dataslices similar to the set of encoded data slices of data segment 1. Forinstance, the content of the first encoded data slice (DS2_d3&4) of thesecond set of encoded data slices (e.g., corresponding to data segment2) is substantially similar to content of the first word (e.g., d3 &d4); the content of the second encoded data slice (DS2_d18&19) of thesecond set of encoded data slices is substantially similar to content ofthe second word (e.g., d18 & d19); and the content of the third encodeddata slice (DS2_d33&34) of the second set of encoded data slices issubstantially similar to content of the third word (e.g., d33 & d34).The content of the fourth and fifth encoded data slices (e.g., ES1_(—)1and ES1_(—)2) of the second set of encoded data slices includes errorcorrection data based on the first-third words of the second datasegment.

FIG. 9 is a diagram of an example of grouping selection processing of anoutbound distributed storage and task (DST) processing in accordancewith group selection information as control information 160 from acontrol module. Encoded slices for data partition 122 are grouped inaccordance with the control information 160 to produce slice groupings96. In this example, a grouping selection module 114 organizes theencoded data slices into five slice groupings (e.g., one for each DSTexecution unit of a distributed storage and task network (DSTN) module).As a specific example, the grouping selection module 114 creates a firstslice grouping for a DST execution unit #1, which includes first encodedslices of each of the sets of encoded slices. As such, the first DSTexecution unit receives encoded data slices corresponding to data blocks1-15 (e.g., encoded data slices of contiguous data).

The grouping selection module 114 also creates a second slice groupingfor a DST execution unit #2, which includes second encoded slices ofeach of the sets of encoded slices. As such, the second DST executionunit receives encoded data slices corresponding to data blocks 16-30.The grouping selection module 114 further creates a third slice groupingfor DST execution unit #3, which includes third encoded slices of eachof the sets of encoded slices. As such, the third DST execution unitreceives encoded data slices corresponding to data blocks 31-45.

The grouping selection module 114 creates a fourth slice grouping forDST execution unit #4, which includes fourth encoded slices of each ofthe sets of encoded slices. As such, the fourth DST execution unitreceives encoded data slices corresponding to first error encodinginformation (e.g., encoded data slices of error coding (EC) data). Thegrouping selection module 114 further creates a fifth slice grouping forDST execution unit #5, which includes fifth encoded slices of each ofthe sets of encoded slices. As such, the fifth DST execution unitreceives encoded data slices corresponding to second error encodinginformation.

FIG. 10 is a diagram of an example of converting data 92 into slicegroups that expands on the preceding figures. As shown, the data 92 ispartitioned in accordance with a partitioning function 164 into aplurality of data partitions (1−x, where x is an integer greater than4). Each data partition (or chunkset of data) is encoded and groupedinto slice groupings as previously discussed by an encoding and groupingfunction 166. For a given data partition, the slice groupings are sentto distributed storage and task (DST) execution units. From datapartition to data partition, the ordering of the slice groupings to theDST execution units may vary. For example, the slice groupings of datapartition #1 is sent to the DST execution units such that the first DSTexecution receives first encoded data slices of each of the sets ofencoded data slices, which corresponds to a first continuous data chunkof the first data partition (e.g., refer to FIG. 9), a second DSTexecution receives second encoded data slices of each of the sets ofencoded data slices, which corresponds to a second continuous data chunkof the first data partition, etc.

For the second data partition, the slice groupings may be sent to theDST execution units in a different order than it was done for the firstdata partition. For instance, the first slice grouping of the seconddata partition (e.g., slice group 2_(—)1) is sent to the second DSTexecution unit; the second slice grouping of the second data partition(e.g., slice group 2_(—)2) is sent to the third DST execution unit; thethird slice grouping of the second data partition (e.g., slice group2_(—)3) is sent to the fourth DST execution unit; the fourth slicegrouping of the second data partition (e.g., slice group 2_(—)4, whichincludes first error coding information) is sent to the fifth DSTexecution unit; and the fifth slice grouping of the second datapartition (e.g., slice group 2_(—)5, which includes second error codinginformation) is sent to the first DST execution unit. The pattern ofsending the slice groupings to the set of DST execution units may varyin a predicted pattern, a random pattern, and/or a combination thereoffrom data partition to data partition. In addition, from data partitionto data partition, the set of DST execution units may change. Forexample, for the first data partition, DST execution units 1-5 may beused; for the second data partition, DST execution units 6-10 may beused; for the third data partition, DST execution units 3-7 may be used;etc. As is also shown, the task is divided into partial tasks that aresent to the DST execution units in conjunction with the slice groupingsof the data partitions.

FIG. 11 is a schematic block diagram of an embodiment of a DST(distributed storage and/or task) execution unit that includes aninterface 169, a controller 86, memory 88, one or more DT (distributedtask) execution modules 90, and a DST client module 34. The memory 88 isof sufficient size to store a significant number of encoded data slices(e.g., thousands of slices to hundreds-of-millions of slices) and mayinclude one or more hard drives and/or one or more solid-state memorydevices (e.g., flash memory, DRAM, etc.).

In an example of storing a slice group, the DST execution modulereceives a slice grouping 96 (e.g., slice group #1) via interface 169.The slice grouping 96 includes, per partition, encoded data slices ofcontiguous data or encoded data slices of error coding (EC) data. Forslice group #1, the DST execution module receives encoded data slices ofcontiguous data for partitions #1 and #x (and potentially others between3 and x) and receives encoded data slices of EC data for partitions #2and #3 (and potentially others between 3 and x). Examples of encodeddata slices of contiguous data and encoded data slices of error coding(EC) data are discussed with reference to FIG. 9. The memory 88 storesthe encoded data slices of slice groupings 96 in accordance with memorycontrol information 174 it receives from the controller 86.

The controller 86 (e.g., a processing module, a CPU, etc.) generates thememory control information 174 based on a partial task(s) 98 anddistributed computing information (e.g., user information (e.g., userID, distributed computing permissions, data access permission, etc.),vault information (e.g., virtual memory assigned to user, user group,temporary storage for task processing, etc.), task validationinformation, etc.). For example, the controller 86 interprets thepartial task(s) 98 in light of the distributed computing information todetermine whether a requestor is authorized to perform the task 98, isauthorized to access the data, and/or is authorized to perform the taskon this particular data. When the requestor is authorized, thecontroller 86 determines, based on the task 98 and/or another input,whether the encoded data slices of the slice grouping 96 are to betemporarily stored or permanently stored. Based on the foregoing, thecontroller 86 generates the memory control information 174 to write theencoded data slices of the slice grouping 96 into the memory 88 and toindicate whether the slice grouping 96 is permanently stored ortemporarily stored.

With the slice grouping 96 stored in the memory 88, the controller 86facilitates execution of the partial task(s) 98. In an example, thecontroller 86 interprets the partial task 98 in light of thecapabilities of the DT execution module(s) 90. The capabilities includeone or more of MIPS capabilities, processing resources (e.g., quantityand capability of microprocessors, CPUs, digital signal processors,co-processor, microcontrollers, arithmetic logic circuitry, and/or theother analog and/or digital processing circuitry), availability of theprocessing resources, etc. If the controller 86 determines that the DTexecution module(s) 90 have sufficient capabilities, it generates taskcontrol information 176.

The task control information 176 may be a generic instruction (e.g.,perform the task on the stored slice grouping) or a series ofoperational codes. In the former instance, the DT execution module 90includes a co-processor function specifically configured (fixed orprogrammed) to perform the desired task 98. In the latter instance, theDT execution module 90 includes a general processor topology where thecontroller stores an algorithm corresponding to the particular task 98.In this instance, the controller 86 provides the operational codes(e.g., assembly language, source code of a programming language, objectcode, etc.) of the algorithm to the DT execution module 90 forexecution.

Depending on the nature of the task 98, the DT execution module 90 maygenerate intermediate partial results 102 that are stored in the memory88 or in a cache memory (not shown) within the DT execution module 90.In either case, when the DT execution module 90 completes execution ofthe partial task 98, it outputs one or more partial results 102. Thepartial results may 102 also be stored in memory 88.

If, when the controller 86 is interpreting whether capabilities of theDT execution module(s) 90 can support the partial task 98, thecontroller 86 determines that the DT execution module(s) 90 cannotadequately support the task 98 (e.g., does not have the right resources,does not have sufficient available resources, available resources wouldbe too slow, etc.), it then determines whether the partial task 98should be fully offloaded or partially offloaded.

If the controller 86 determines that the partial task 98 should be fullyoffloaded, it generates DST control information 178 and provides it tothe DST client module 34. The DST control information 178 includes thepartial task 98, memory storage information regarding the slice grouping96, and distribution instructions. The distribution instructionsinstruct the DST client module 34 to divide the partial task 98 intosub-partial tasks 172, to divide the slice grouping 96 into sub-slicegroupings 170, and identity of other DST execution units. The DST clientmodule 34 functions in a similar manner as the DST client module 34 ofFIGS. 3-10 to produce the sub-partial tasks 172 and the sub-slicegroupings 170 in accordance with the distribution instructions.

The DST client module 34 receives DST feedback 168 (e.g., sub-partialresults), via the interface 169, from the DST execution units to whichthe task was offloaded. The DST client module 34 provides thesub-partial results to the DST execution unit, which processes thesub-partial results to produce the partial result(s) 102.

If the controller 86 determines that the partial task 98 should bepartially offloaded, it determines what portion of the task 98 and/orslice grouping 96 should be processed locally and what should beoffloaded. For the portion that is being locally processed, thecontroller 86 generates task control information 176 as previouslydiscussed. For the portion that is being offloaded, the controller 86generates DST control information 178 as previously discussed.

When the DST client module 34 receives DST feedback 168 (e.g.,sub-partial results) from the DST executions units to which a portion ofthe task was offloaded, it provides the sub-partial results to the DTexecution module 90. The DT execution module 90 processes thesub-partial results with the sub-partial results it created to producethe partial result(s) 102.

The memory 88 may be further utilized to retrieve one or more of storedslices 100, stored results 104, partial results 102 when the DTexecution module 90 stores partial results 102 and/or results 104 andthe memory 88. For example, when the partial task 98 includes aretrieval request, the controller 86 outputs the memory control 174 tothe memory 88 to facilitate retrieval of slices 100 and/or results 104.

FIG. 12 is a schematic block diagram of an example of operation of adistributed storage and task (DST) execution unit storing encoded dataslices and executing a task thereon. To store the encoded data slices ofa partition 1 of slice grouping 1, a controller 86 generates writecommands as memory control information 174 such that the encoded slicesare stored in desired locations (e.g., permanent or temporary) withinmemory 88.

Once the encoded slices are stored, the controller 86 provides taskcontrol information 176 to a distributed task (DT) execution module 90.As a first step executing the task in accordance with the task controlinformation 176, the DT execution module 90 retrieves the encoded slicesfrom memory 88. The DT execution module 90 then reconstructs contiguousdata blocks of a data partition. As shown for this example,reconstructed contiguous data blocks of data partition 1 include datablocks 1-15 (e.g., d1-d15).

With the contiguous data blocks reconstructed, the DT execution module90 performs the task on the reconstructed contiguous data blocks. Forexample, the task may be to search the reconstructed contiguous datablocks for a particular word or phrase, identify where in thereconstructed contiguous data blocks the particular word or phraseoccurred, and/or count the occurrences of the particular word or phraseon the reconstructed contiguous data blocks. The DST execution unitcontinues in a similar manner for the encoded data slices of otherpartitions in slice grouping 1. Note that with using the unity matrixerror encoding scheme previously discussed, if the encoded data slicesof contiguous data are uncorrupted, the decoding of them is a relativelystraightforward process of extracting the data.

If, however, an encoded data slice of contiguous data is corrupted (ormissing), it can be rebuilt by accessing other DST execution units thatare storing the other encoded data slices of the set of encoded dataslices of the corrupted encoded data slice. In this instance, the DSTexecution unit having the corrupted encoded data slices retrieves atleast three encoded data slices (of contiguous data and of error codingdata) in the set from the other DST execution units (recall for thisexample, the pillar width is 5 and the decode threshold is 3). The DSTexecution unit decodes the retrieved data slices using the DS errorencoding parameters to recapture the corresponding data segment. The DSTexecution unit then re-encodes the data segment using the DS errorencoding parameters to rebuild the corrupted encoded data slice. Oncethe encoded data slice is rebuilt, the DST execution unit functions aspreviously described.

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing section 82 of a DSTclient module coupled to DST execution units of a distributed storageand task network (DSTN) module via a network 24. The inbound DSTprocessing section 82 includes a de-grouping module 180, a DS (dispersedstorage) error decoding module 182, a data de-partitioning module 184, acontrol module 186, and a distributed task control module 188. Note thatthe control module 186 and/or the distributed task control module 188may be separate modules from corresponding ones of outbound DSTprocessing section or may be the same modules.

In an example of operation, the DST execution units have completedexecution of corresponding partial tasks on the corresponding slicegroupings to produce partial results 102. The inbounded DST processingsection 82 receives the partial results 102 via the distributed taskcontrol module 188. The inbound DST processing section 82 then processesthe partial results 102 to produce a final result, or results 104. Forexample, if the task was to find a specific word or phrase within data,the partial results 102 indicate where in each of the prescribedportions of the data the corresponding DST execution units found thespecific word or phrase. The distributed task control module 188combines the individual partial results 102 for the correspondingportions of the data into a final result 104 for the data as a whole.

In another example of operation, the inbound DST processing section 82is retrieving stored data from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices 100 corresponding to the data retrieval requests. The de-groupingmodule 180 receives retrieved slices 100 and de-groups them to produceencoded data slices per data partition 122. The DS error decoding module182 decodes, in accordance with DS error encoding parameters, theencoded data slices per data partition 122 to produce data partitions120.

The data de-partitioning module 184 combines the data partitions 120into the data 92. The control module 186 controls the conversion ofretrieve slices 100 into the data 92 using control signals 190 to eachof the modules. For instance, the control module 186 providesde-grouping information to the de-grouping module 180, provides the DSerror encoding parameters to the DS error decoding module 182, andprovides de-partitioning information to the data de-partitioning module184.

FIG. 14 is a logic diagram of an example of a method that is executableby distributed storage and task (DST) client module regarding inboundDST processing. The method begins at step 194 where the DST clientmodule receives partial results. The method continues at step 196 wherethe DST client module retrieves the task corresponding to the partialresults. For example, the partial results include header informationthat identifies the requesting entity, which correlates to the requestedtask.

The method continues at step 198 where the DST client module determinesresult processing information based on the task. For example, if thetask were to identify a particular word or phrase within the data, theresult processing information would indicate to aggregate the partialresults for the corresponding portions of the data to produce the finalresult. As another example, if the task were to count the occurrences ofa particular word or phrase within the data, results of processing theinformation would indicate to add the partial results to produce thefinal results. The method continues at step 200 where the DST clientmodule processes the partial results in accordance with the resultprocessing information to produce the final result or results.

FIG. 15 is a diagram of an example of de-grouping selection processingof an inbound distributed storage and task (DST) processing section of aDST client module. In general, this is an inverse process of thegrouping module of the outbound DST processing section of FIG. 9.Accordingly, for each data partition (e.g., partition #1), thede-grouping module retrieves the corresponding slice grouping from theDST execution units (EU) (e.g., DST 1-5).

As shown, DST execution unit #1 provides a first slice grouping, whichincludes the first encoded slices of each of the sets of encoded slices(e.g., encoded data slices of contiguous data of data blocks 1-15); DSTexecution unit #2 provides a second slice grouping, which includes thesecond encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 16-30); DSTexecution unit #3 provides a third slice grouping, which includes thethird encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 31-45); DSTexecution unit #4 provides a fourth slice grouping, which includes thefourth encoded slices of each of the sets of encoded slices (e.g., firstencoded data slices of error coding (EC) data); and DST execution unit#5 provides a fifth slice grouping, which includes the fifth encodedslices of each of the sets of encoded slices (e.g., first encoded dataslices of error coding (EC) data).

The de-grouping module de-groups the slice groupings (e.g., receivedslices 100) using a de-grouping selector 180 controlled by a controlsignal 190 as shown in the example to produce a plurality of sets ofencoded data slices (e.g., retrieved slices for a partition into sets ofslices 122). Each set corresponding to a data segment of the datapartition.

FIG. 16 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, a de-segmenting processing module 210, and acontrol module 186.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186, unsecures eachencoded data slice 122 based on slice de-security information receivedas control information 190 (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received from thecontrol module 186. The slice security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRCverification, etc.), and/or any other type of digital security. Forexample, when the inverse per slice security processing module 202 isenabled, it verifies integrity information (e.g., a CRC value) of eachencoded data slice 122, it decrypts each verified encoded data slice,and decompresses each decrypted encoded data slice to produce sliceencoded data 158. When the inverse per slice security processing module202 is not enabled, it passes the encoded data slices 122 as the slicedencoded data 158 or is bypassed such that the retrieved encoded dataslices 122 are provided as the sliced encoded data 158.

The de-slicing module 204 de-slices the sliced encoded data 158 intoencoded data segments 156 in accordance with a pillar width of the errorcorrection encoding parameters received as control information 190 fromthe control module 186. For example, if the pillar width is five, thede-slicing module 204 de-slices a set of five encoded data slices intoan encoded data segment 156. The error decoding module 206 decodes theencoded data segments 156 in accordance with error correction decodingparameters received as control information 190 from the control module186 to produce secure data segments 154. The error correction decodingparameters include identifying an error correction encoding scheme(e.g., forward error correction algorithm, a Reed-Salomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction decoding parameters identify a specific errorcorrection encoding scheme, specify a pillar width of five, and specifya decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments 154 based onsegment security information received as control information 190 fromthe control module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module 208 isenabled, it verifies integrity information (e.g., a CRC value) of eachsecure data segment 154, it decrypts each verified secured data segment,and decompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 154 as the data segment152 or is bypassed.

The de-segment processing module 210 receives the data segments 152 andreceives de-segmenting information as control information 190 from thecontrol module 186. The de-segmenting information indicates how thede-segment processing module 210 is to de-segment the data segments 152into a data partition 120. For example, the de-segmenting informationindicates how the rows and columns of data segments are to be rearrangedto yield the data partition 120.

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of a dispersed error decoding module. A de-slicing module 204receives at least a decode threshold number of encoded data slices 158for each data segment in accordance with control information 190 andprovides encoded data 156. In this example, a decode threshold is three.As such, each set of encoded data slices 158 is shown to have threeencoded data slices per data segment. The de-slicing module 204 mayreceive three encoded data slices per data segment because an associateddistributed storage and task (DST) client module requested retrievingonly three encoded data slices per segment or selected three of theretrieved encoded data slices per data segment. As shown, which is basedon the unity matrix encoding previously discussed with reference to FIG.8, an encoded data slice may be a data-based encoded data slice (e.g.,DS1_d1&d2) or an error code based encoded data slice (e.g., ES3_(—)1).

An error decoding module 206 decodes the encoded data 156 of each datasegment in accordance with the error correction decoding parameters ofcontrol information 190 to produce secured segments 154. In thisexample, data segment 1 includes 3 rows with each row being treated asone word for encoding. As such, data segment 1 includes three words:word 1 including data blocks d1 and d2, word 2 including data blocks d16and d17, and word 3 including data blocks d31 and d32. Each of datasegments 2-7 includes three words where each word includes two datablocks. Data segment 8 includes three words where each word includes asingle data block (e.g., d15, d30, and d45).

FIG. 18 is a diagram of an example of a de-segment processing of aninbound distributed storage and task (DST) processing. In this example,a de-segment processing module 210 receives data segments 152 (e.g.,1-8) and rearranges the data blocks of the data segments into rows andcolumns in accordance with de-segmenting information of controlinformation 190 to produce a data partition 120. Note that the number ofrows is based on the decode threshold (e.g., 3 in this specific example)and the number of columns is based on the number and size of the datablocks.

The de-segmenting module 210 converts the rows and columns of datablocks into the data partition 120. Note that each data block may be ofthe same size as other data blocks or of a different size. In addition,the size of each data block may be a few bytes to megabytes of data.

FIG. 19 is a diagram of an example of converting slice groups into data92 within an inbound distributed storage and task (DST) processingsection. As shown, the data 92 is reconstructed from a plurality of datapartitions (1-x, where x is an integer greater than 4). Each datapartition (or chunk set of data) is decoded and re-grouped using ade-grouping and decoding function 212 and a de-partition function 214from slice groupings as previously discussed. For a given datapartition, the slice groupings (e.g., at least a decode threshold perdata segment of encoded data slices) are received from DST executionunits. From data partition to data partition, the ordering of the slicegroupings received from the DST execution units may vary as discussedwith reference to FIG. 10.

FIG. 20 is a diagram of an example of a distributed storage and/orretrieval within the distributed computing system. The distributedcomputing system includes a plurality of distributed storage and/or task(DST) processing client modules 34 (one shown) coupled to a distributedstorage and/or task processing network (DSTN) module, or multiple DSTNmodules, via a network 24. The DST client module 34 includes an outboundDST processing section 80 and an inbound DST processing section 82. TheDSTN module includes a plurality of DST execution units. Each DSTexecution unit includes a controller 86, memory 88, one or moredistributed task (DT) execution modules 90, and a DST client module 34.

In an example of data storage, the DST client module 34 has data 92 thatit desires to store in the DSTN module. The data 92 may be a file (e.g.,video, audio, text, graphics, etc.), a data object, a data block, anupdate to a file, an update to a data block, etc. In this instance, theoutbound DST processing module 80 converts the data 92 into encoded dataslices 216 as will be further described with reference to FIGS. 21-23.The outbound DST processing module 80 sends, via the network 24, to theDST execution units for storage as further described with reference toFIG. 24.

In an example of data retrieval, the DST client module 34 issues aretrieve request to the DST execution units for the desired data 92. Theretrieve request may address each DST executions units storing encodeddata slices of the desired data, address a decode threshold number ofDST execution units, address a read threshold number of DST executionunits, or address some other number of DST execution units. In responseto the request, each addressed DST execution unit retrieves its encodeddata slices 100 of the desired data and sends them to the inbound DSTprocessing section 82, via the network 24.

When, for each data segment, the inbound DST processing section 82receives at least a decode threshold number of encoded data slices 100,it converts the encoded data slices 100 into a data segment. The inboundDST processing section 82 aggregates the data segments to produce theretrieved data 92.

FIG. 21 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module coupled to a distributed storage and task network (DSTN)module (e.g., a plurality of DST execution units) via a network 24. Theoutbound DST processing section 80 includes a data partitioning module110, a dispersed storage (DS) error encoding module 112, a groupselection module 114, a control module 116, and a distributed taskcontrol module 118.

In an example of operation, the data partitioning module 110 isby-passed such that data 92 is provided directly to the DS errorencoding module 112. The control module 116 coordinates the by-passingof the data partitioning module 110 by outputting a bypass 220 messageto the data partitioning module 110.

The DS error encoding module 112 receives the data 92 in a serialmanner, a parallel manner, and/or a combination thereof. The DS errorencoding module 112 DS error encodes the data in accordance with controlinformation 160 from the control module 116 to produce encoded dataslices 218. The DS error encoding includes segmenting the data 92 intodata segments, segment security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC, etc.)), errorencoding, slicing, and/or per slice security processing (e.g.,encryption, compression, watermarking, integrity check (e.g., CRC,etc.)). The control information 160 indicates which steps of the DSerror encoding are active for the data 92 and, for active steps,indicates the parameters for the step. For example, the controlinformation 160 indicates that the error encoding is active and includeserror encoding parameters (e.g., pillar width, decode threshold, writethreshold, read threshold, type of error encoding, etc.).

The group selecting module 114 groups the encoded slices 218 of the datasegments into pillars of slices 216. The number of pillars correspondsto the pillar width of the DS error encoding parameters. In thisexample, the distributed task control module 118 facilitates the storagerequest.

FIG. 22 is a schematic block diagram of an example of a dispersedstorage (DS) error encoding module 112 for the example of FIG. 21. TheDS error encoding module 112 includes a segment processing module 142, asegment security processing module 144, an error encoding module 146, aslicing module 148, and a per slice security processing module 150. Eachof these modules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receivesdata 92 and receives segmenting information as control information 160from the control module 116. The segmenting information indicates howthe segment processing module is to segment the data. For example, thesegmenting information indicates the size of each data segment. Thesegment processing module 142 segments the data 92 into data segments152 in accordance with the segmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., CRC, etc.), and/or anyother type of digital security. For example, when the segment securityprocessing module 144 is enabled, it compresses a data segment 152,encrypts the compressed data segment, and generates a CRC value for theencrypted data segment to produce a secure data segment. When thesegment security processing module 144 is not enabled, it passes thedata segments 152 to the error encoding module 146 or is bypassed suchthat the data segments 152 are provided to the error encoding module146.

The error encoding module 146 encodes the secure data segments inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Salomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction encoding parameters identify a specific errorcorrection encoding scheme, specifies a pillar width of five, andspecifies a decode threshold of three. From these parameters, the errorencoding module 146 encodes a data segment to produce an encoded datasegment.

The slicing module 148 slices the encoded data segment in accordancewith a pillar width of the error correction encoding parameters. Forexample, if the pillar width is five, the slicing module slices anencoded data segment into a set of five encoded data slices. As such,for a plurality of data segments, the slicing module 148 outputs aplurality of sets of encoded data slices as shown within encoding andslicing function 222 as described.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it may compress anencoded data slice, encrypt the compressed encoded data slice, andgenerate a CRC value for the encrypted encoded data slice to produce asecure encoded data slice tweaking. When the per slice securityprocessing module 150 is not enabled, it passes the encoded data slicesor is bypassed such that the encoded data slices 218 are the output ofthe DS error encoding module 112.

FIG. 23 is a diagram of an example of converting data 92 into pillarslice groups utilizing encoding, slicing and pillar grouping function224 for storage in memory of a distributed storage and task network(DSTN) module. As previously discussed the data 92 is encoded and slicedinto a plurality of sets of encoded data slices; one set per datasegment. The grouping selection module organizes the sets of encodeddata slices into pillars of data slices. In this example, the DS errorencoding parameters include a pillar width of 5 and a decode thresholdof 3. As such, for each data segment, 5 encoded data slices are created.The grouping selection module takes the first encoded data slice of eachof the sets and forms a first pillar, which may be sent to the first DSTexecution unit. Similarly, the grouping selection module creates thesecond pillar from the second slices of the sets; the third pillar fromthe third slices of the sets; the fourth pillar from the fourth slicesof the sets; and the fifth pillar from the fifth slices of the set.

FIG. 24 is a schematic block diagram of an embodiment of a distributedstorage and/or task (DST) execution unit that includes an interface 169,a controller 86, memory 88, one or more distributed task (DT) executionmodules 90, and a DST client module 34. A computing core 26 may beutilized to implement the one or more DT execution modules 90 and theDST client module 34. The memory 88 is of sufficient size to store asignificant number of encoded data slices (e.g., thousands of slices tohundreds-of-millions of slices) and may include one or more hard drivesand/or one or more solid-state memory devices (e.g., flash memory, DRAM,etc.).

In an example of storing a pillar of slices 216, the DST execution unitreceives, via interface 169, a pillar of slices 216 (e.g., pillar #1slices). The memory 88 stores the encoded data slices 216 of the pillarof slices in accordance with memory control information 174 it receivesfrom the controller 86. The controller 86 (e.g., a processing module, aCPU, etc.) generates the memory control information 174 based ondistributed storage information (e.g., user information (e.g., user ID,distributed storage permissions, data access permission, etc.), vaultinformation (e.g., virtual memory assigned to user, user group, etc.),etc.). Similarly, when retrieving slices, the DST execution unitreceives, via interface 169, a slice retrieval request. The memory 88retrieves the slice in accordance with memory control information 174 itreceives from the controller 86. The memory 88 outputs the slice 100,via the interface 169, to a requesting entity.

FIG. 25 is a schematic block diagram of an example of operation of aninbound distributed storage and/or task (DST) processing section 82 forretrieving dispersed error encoded data 92. The inbound DST processingsection 82 includes a de-grouping module 180, a dispersed storage (DS)error decoding module 182, a data de-partitioning module 184, a controlmodule 186, and a distributed task control module 188. Note that thecontrol module 186 and/or the distributed task control module 188 may beseparate modules from corresponding ones of an outbound DST processingsection or may be the same modules.

In an example of operation, the inbound DST processing section 82 isretrieving stored data 92 from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices corresponding to data retrieval requests from the distributedtask control module 188. The de-grouping module 180 receives pillars ofslices 100 and de-groups them in accordance with control information 190from the control module 186 to produce sets of encoded data slices 218.The DS error decoding module 182 decodes, in accordance with the DSerror encoding parameters received as control information 190 from thecontrol module 186, each set of encoded data slices 218 to produce datasegments, which are aggregated into retrieved data 92. The datade-partitioning module 184 is by-passed in this operational mode via abypass signal 226 of control information 190 from the control module186.

FIG. 26 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, and a de-segmenting processing module 210. Thedispersed error decoding module 182 is operable to de-slice and decodeencoded slices per data segment 218 utilizing a de-slicing and decodingfunction 228 to produce a plurality of data segments that arede-segmented utilizing a de-segment function 230 to recover data 92.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186 via controlinformation 190, unsecures each encoded data slice 218 based on slicede-security information (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received as controlinformation 190 from the control module 186. The slice de-securityinformation includes data decompression, decryption, de-watermarking,integrity check (e.g., CRC verification, etc.), and/or any other type ofdigital security. For example, when the inverse per slice securityprocessing module 202 is enabled, it verifies integrity information(e.g., a CRC value) of each encoded data slice 218, it decrypts eachverified encoded data slice, and decompresses each decrypted encodeddata slice to produce slice encoded data. When the inverse per slicesecurity processing module 202 is not enabled, it passes the encodeddata slices 218 as the sliced encoded data or is bypassed such that theretrieved encoded data slices 218 are provided as the sliced encodeddata.

The de-slicing module 204 de-slices the sliced encoded data into encodeddata segments in accordance with a pillar width of the error correctionencoding parameters received as control information 190 from a controlmodule 186. For example, if the pillar width is five, the de-slicingmodule de-slices a set of five encoded data slices into an encoded datasegment. Alternatively, the encoded data segment may include just threeencoded data slices (e.g., when the decode threshold is 3).

The error decoding module 206 decodes the encoded data segments inaccordance with error correction decoding parameters received as controlinformation 190 from the control module 186 to produce secure datasegments. The error correction decoding parameters include identifyingan error correction encoding scheme (e.g., forward error correctionalgorithm, a Reed-Salomon based algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction decodingparameters identify a specific error correction encoding scheme, specifya pillar width of five, and specify a decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments based on segmentsecurity information received as control information 190 from thecontrol module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module is enabled,it verifies integrity information (e.g., a CRC value) of each securedata segment, it decrypts each verified secured data segment, anddecompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 152 as the data segmentor is bypassed. The de-segmenting processing module 210 aggregates thedata segments 152 into the data 92 in accordance with controlinformation 190 from the control module 186.

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module that includes aplurality of distributed storage and task (DST) execution units (#1through #n, where, for example, n is an integer greater than or equal tothree). Each of the DST execution units includes a DST client module 34,a controller 86, one or more DT (distributed task) execution modules 90,and memory 88. In this example, the DSTN module stores, in the memory ofthe DST execution units, a plurality of DS (dispersed storage) encodeddata (e.g., 1 through n, where n is an integer greater than or equal totwo) and stores a plurality of DS encoded task codes (e.g., 1 through k,where k is an integer greater than or equal to two). The DS encoded datamay be encoded in accordance with one or more examples described withreference to FIGS. 3-19 (e.g., organized in slice groupings) or encodedin accordance with one or more examples described with reference toFIGS. 20-26 (e.g., organized in pillar groups). The data that is encodedinto the DS encoded data may be of any size and/or of any content. Forexample, the data may be one or more digital books, a copy of acompany's emails, a large-scale Internet search, a video security file,one or more entertainment video files (e.g., television programs,movies, etc.), data files, and/or any other large amount of data (e.g.,greater than a few Terra-Bytes).

The tasks that are encoded into the DS encoded task code may be a simplefunction (e.g., a mathematical function, a logic function, an identifyfunction, a find function, a search engine function, a replace function,etc.), a complex function (e.g., compression, human and/or computerlanguage translation, text-to-voice conversion, voice-to-textconversion, etc.), multiple simple and/or complex functions, one or morealgorithms, one or more applications, etc. The tasks may be encoded intothe DS encoded task code in accordance with one or more examplesdescribed with reference to FIGS. 3-19 (e.g., organized in slicegroupings) or encoded in accordance with one or more examples describedwith reference to FIGS. 20-26 (e.g., organized in pillar groups).

In an example of operation, a DST client module of a user device or of aDST processing unit issues a DST request to the DSTN module. The DSTrequest may include a request to retrieve stored data, or a portionthereof, may include a request to store data that is included with theDST request, may include a request to perform one or more tasks onstored data, may include a request to perform one or more tasks on dataincluded with the DST request, etc. In the cases where the DST requestincludes a request to store data or to retrieve data, the client moduleand/or the DSTN module processes the request as previously discussedwith reference to one or more of FIGS. 3-19 (e.g., slice groupings)and/or 20-26 (e.g., pillar groupings). In the case where the DST requestincludes a request to perform one or more tasks on data included withthe DST request, the DST client module and/or the DSTN module processthe DST request as previously discussed with reference to one or more ofFIGS. 3-19.

In the case where the DST request includes a request to perform one ormore tasks on stored data, the DST client module and/or the DSTN moduleprocesses the DST request as will be described with reference to one ormore of FIGS. 28-39. In general, the DST client module identifies dataand one or more tasks for the DSTN module to execute upon the identifieddata. The DST request may be for a one-time execution of the task or foran on-going execution of the task. As an example of the latter, as acompany generates daily emails, the DST request may be to daily searchnew emails for inappropriate content and, if found, record the content,the email sender(s), the email recipient(s), email routing information,notify human resources of the identified email, etc.

FIG. 28 is a schematic block diagram of an example of a distributedcomputing system performing tasks on stored data. In this example, twodistributed storage and task (DST) client modules 1-2 are shown: thefirst may be associated with a user device and the second may beassociated with a DST processing unit or a high priority user device(e.g., high priority clearance user, system administrator, etc.). EachDST client module includes a list of stored data 234 and a list of taskscodes 236. The list of stored data 234 includes one or more entries ofdata identifying information, where each entry identifies data stored inthe DSTN module 22. The data identifying information (e.g., data ID)includes one or more of a data file name, a data file directory listing,DSTN addressing information of the data, a data object identifier, etc.The list of tasks 236 includes one or more entries of task codeidentifying information, when each entry identifies task codes stored inthe DSTN module 22. The task code identifying information (e.g., taskID) includes one or more of a task file name, a task file directorylisting, DSTN addressing information of the task, another type ofidentifier to identify the task, etc.

As shown, the list of data 234 and the list of tasks 236 are eachsmaller in number of entries for the first DST client module than thecorresponding lists of the second DST client module. This may occurbecause the user device associated with the first DST client module hasfewer privileges in the distributed computing system than the deviceassociated with the second DST client module. Alternatively, this mayoccur because the user device associated with the first DST clientmodule serves fewer users than the device associated with the second DSTclient module and is restricted by the distributed computing systemaccordingly. As yet another alternative, this may occur through norestraints by the distributed computing system, it just occurred becausethe operator of the user device associated with the first DST clientmodule has selected fewer data and/or fewer tasks than the operator ofthe device associated with the second DST client module.

In an example of operation, the first DST client module selects one ormore data entries 238 and one or more tasks 240 from its respectivelists (e.g., selected data ID and selected task ID). The first DSTclient module sends its selections to a task distribution module 232.The task distribution module 232 may be within a stand-alone device ofthe distributed computing system, may be within the user device thatcontains the first DST client module, or may be within the DSTN module22.

Regardless of the task distributions modules location, it generates DSTallocation information 242 from the selected task ID 240 and theselected data ID 238. The DST allocation information 242 includes datapartitioning information, task execution information, and/orintermediate result information. The task distribution module 232 sendsthe DST allocation information 242 to the DSTN module 22. Note that oneor more examples of the DST allocation information will be discussedwith reference to one or more of FIGS. 29-39.

The DSTN module 22 interprets the DST allocation information 242 toidentify the stored DS encoded data (e.g., DS error encoded data 2) andto identify the stored DS error encoded task code (e.g., DS errorencoded task code 1). In addition, the DSTN module 22 interprets the DSTallocation information 242 to determine how the data is to bepartitioned and how the task is to be partitioned. The DSTN module 22also determines whether the selected DS error encoded data 238 needs tobe converted from pillar grouping to slice grouping. If so, the DSTNmodule 22 converts the selected DS error encoded data into slicegroupings and stores the slice grouping DS error encoded data byoverwriting the pillar grouping DS error encoded data or by storing itin a different location in the memory of the DSTN module 22 (i.e., doesnot overwrite the pillar grouping DS encoded data).

The DSTN module 22 partitions the data and the task as indicated in theDST allocation information 242 and sends the portions to selected DSTexecution units of the DSTN module 22. Each of the selected DSTexecution units performs its partial task(s) on its slice groupings toproduce partial results. The DSTN module 22 collects the partial resultsfrom the selected DST execution units and provides them, as resultinformation 244, to the task distribution module. The result information244 may be the collected partial results, one or more final results asproduced by the DSTN module 22 from processing the partial results inaccordance with the DST allocation information 242, or one or moreintermediate results as produced by the DSTN module 22 from processingthe partial results in accordance with the DST allocation information242.

The task distribution module 232 receives the result information 244 andprovides one or more final results 104 therefrom to the first DST clientmodule. The final result(s) 104 may be result information 244 or aresult(s) of the task distribution module's processing of the resultinformation 244.

In concurrence with processing the selected task of the first DST clientmodule, the distributed computing system may process the selectedtask(s) of the second DST client module on the selected data(s) of thesecond DST client module. Alternatively, the distributed computingsystem may process the second DST client module's request subsequent to,or preceding, that of the first DST client module. Regardless of theordering and/or parallel processing of the DST client module requests,the second DST client module provides its selected data 238 and selectedtask 240 to a task distribution module 232. If the task distributionmodule 232 is a separate device of the distributed computing system orwithin the DSTN module, the task distribution modules 232 coupled to thefirst and second DST client modules may be the same module. The taskdistribution module 232 processes the request of the second DST clientmodule in a similar manner as it processed the request of the first DSTclient module.

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module 232 facilitating the example of FIG. 28. The taskdistribution module 232 includes a plurality of tables it uses togenerate distributed storage and task (DST) allocation information 242for selected data and selected tasks received from a DST client module.The tables include data storage information 248, task storageinformation 250, distributed task (DT) execution module information 252,and task

sub-task mapping information 246.

The data storage information table 248 includes a data identification(ID) field 260, a data size field 262, an addressing information field264, distributed storage (DS) information 266, and may further includeother information regarding the data, how it is stored, and/or how itcan be processed. For example, DS encoded data #1 has a data ID of 1, adata size of AA (e.g., a byte size of a few terra-bytes or more),addressing information of Addr_(—)1_AA, and DS parameters of 3/5;SEG_(—)1; and SLC_(—)1. In this example, the addressing information maybe a virtual address corresponding to the virtual address of the firststorage word (e.g., one or more bytes) of the data and information onhow to calculate the other addresses, may be a range of virtualaddresses for the storage words of the data, physical addresses of thefirst storage word or the storage words of the data, may be a list ofslice names of the encoded data slices of the data, etc. The DSparameters may include identity of an error encoding scheme, decodethreshold/pillar width (e.g., 3/5 for the first data entry), segmentsecurity information (e.g., SEG_(—)1), per slice security information(e.g., SLC_(—)1), and/or any other information regarding how the datawas encoded into data slices.

The task storage information table 250 includes a task identification(ID) field 268, a task size field 270, an addressing information field272, distributed storage (DS) information 274, and may further includeother information regarding the task, how it is stored, and/or how itcan be used to process data. For example, DS encoded task #2 has a taskID of 2, a task size of XY, addressing information of Addr_(—)2_XY, andDS parameters of 3/5; SEG_(—)2; and SLC_(—)2. In this example, theaddressing information may be a virtual address corresponding to thevirtual address of the first storage word (e.g., one or more bytes) ofthe task and information on how to calculate the other addresses, may bea range of virtual addresses for the storage words of the task, physicaladdresses of the first storage word or the storage words of the task,may be a list of slices names of the encoded slices of the task code,etc. The DS parameters may include identity of an error encoding scheme,decode threshold/pillar width (e.g., 3/5 for the first data entry),segment security information (e.g., SEG_(—)2), per slice securityinformation (e.g., SLC_(—)2), and/or any other information regarding howthe task was encoded into encoded task slices. Note that the segmentand/or the per-slice security information include a type of encryption(if enabled), a type of compression (if enabled), watermarkinginformation (if enabled), and/or an integrity check scheme (if enabled).

The task

sub-task mapping information table 246 includes a task field 256 and asub-task field 258. The task field 256 identifies a task stored in thememory of a distributed storage and task network (DSTN) module and thecorresponding sub-task fields 258 indicates whether the task includessub-tasks and, if so, how many and if any of the sub-tasks are ordered.In this example, the task

sub-task mapping information table 246 includes an entry for each taskstored in memory of the DSTN module (e.g., task 1 through task k). Inparticular, this example indicates that task 1 includes 7 sub-tasks;task 2 does not include sub-tasks, and task k includes r number ofsub-tasks (where r is an integer greater than or equal to two).

The DT execution module table 252 includes a DST execution unit ID field276, a DT execution module ID field 278, and a DT execution modulecapabilities field 280. The DST execution unit ID field 276 includes theidentity of DST units in the DSTN module. The DT execution module IDfield 278 includes the identity of each DT execution unit in each DSTunit. For example, DST unit 1 includes three DT executions modules(e.g., 1_(—)1, 1_(—)2, and 1_(—)3). The DT execution capabilities field280 includes identity of the capabilities of the corresponding DTexecution unit. For example, DT execution module 1_(—)1 includescapabilities X, where X includes one or more of MIPS capabilities,processing resources (e.g., quantity and capability of microprocessors,CPUs, digital signal processors, co-processor, microcontrollers,arithmetic logic circuitry, and/or other analog and/or digitalprocessing circuitry), availability of the processing resources, memoryinformation (e.g., type, size, availability, etc.), and/or anyinformation germane to executing one or more tasks.

From these tables, the task distribution module 232 generates the DSTallocation information 242 to indicate where the data is stored, how topartition the data, where the task is stored, how to partition the task,which DT execution units should perform which partial task on which datapartitions, where and how intermediate results are to be stored, etc. Ifmultiple tasks are being performed on the same data or different data,the task distribution module factors such information into itsgeneration of the DST allocation information.

FIG. 30 is a diagram of a specific example of a distributed computingsystem performing tasks on stored data as a task flow 318. In thisexample, selected data 92 is data 2 and selected tasks are tasks 1, 2,and 3. Task 1 corresponds to analyzing translation of data from onelanguage to another (e.g., human language or computer language); task 2corresponds to finding specific words and/or phrases in the data; andtask 3 corresponds to finding specific translated words and/or phrasesin translated data.

In this example, task 1 includes 7 sub-tasks: task 1_(—)1—identifynon-words (non-ordered); task 1_(—)2—identify unique words(non-ordered); task 1_(—)3—translate (non-ordered); task1_(—)4—translate back (ordered after task 1_(—)3); task 1_(—)5—compareto ID errors (ordered after task 1-4); task 1_(—)6—determine non-wordtranslation errors (ordered after task 1_(—)5 and 1_(—)1); and task1_(—)7-determine correct translations (ordered after 1_(—)5 and 1_(—)2).The sub-task further indicates whether they are an ordered task (i.e.,are dependent on the outcome of another task) or non-order (i.e., areindependent of the outcome of another task). Task 2 does not includesub-tasks and task 3 includes two sub-tasks: task 3_(—)1 translate; andtask 3_(—)2 find specific word or phrase in translated data.

In general, the three tasks collectively are selected to analyze datafor translation accuracies, translation errors, translation anomalies,occurrence of specific words or phrases in the data, and occurrence ofspecific words or phrases on the translated data. Graphically, the data92 is translated 306 into translated data 282; is analyzed for specificwords and/or phrases 300 to produce a list of specific words and/orphrases 286; is analyzed for non-words 302 (e.g., not in a referencedictionary) to produce a list of non-words 290; and is analyzed forunique words 316 included in the data 92 (i.e., how many different wordsare included in the data) to produce a list of unique words 298. Each ofthese tasks is independent of each other and can therefore be processedin parallel if desired.

The translated data 282 is analyzed (e.g., sub-task 3_(—)2) for specifictranslated words and/or phrases 304 to produce a list of specifictranslated words and/or phrases. The translated data 282 is translatedback 308 (e.g., sub-task 1_(—)4) into the language of the original datato produce re-translated data 284. These two tasks are dependent on thetranslate task (e.g., task 1_(—)3) and thus must be ordered after thetranslation task, which may be in a pipelined ordering or a serialordering. The re-translated data 284 is then compared 310 with theoriginal data 92 to find words and/or phrases that did not translate(one way and/or the other) properly to produce a list of incorrectlytranslated words 294. As such, the comparing task (e.g., sub-task1_(—)5) 310 is ordered after the translation 306 and re-translationtasks 308 (e.g., sub-tasks 1_(—)3 and 1_(—)4).

The list of words incorrectly translated 294 is compared 312 to the listof non-words 290 to identify words that were not properly translatedbecause the words are non-words to produce a list of errors due tonon-words 292. In addition, the list of words incorrectly translated 294is compared 314 to the list of unique words 298 to identify unique wordsthat were properly translated to produce a list of correctly translatedwords 296. The comparison may also identify unique words that were notproperly translated to produce a list of unique words that were notproperly translated. Note that each list of words (e.g., specific wordsand/or phrases, non-words, unique words, translated words and/orphrases, etc.,) may include the word and/or phrase, how many times it isused, where in the data it is used, and/or any other informationrequested regarding a word and/or phrase.

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30. As shown, DS encoded data 2 is storedas encoded data slices across the memory (e.g., stored in memories 88)of DST execution units 1-5; the DS encoded task code 1 (of task 1) andDS encoded task 3 are stored as encoded task slices across the memory ofDST execution units 1-5; and DS encoded task code 2 (of task 2) isstored as encoded task slices across the memory of DST execution units3-7. As indicated in the data storage information table and the taskstorage information table of FIG. 29, the respective data/task has DSparameters of 3/5 for their decode threshold/pillar width; hencespanning the memory of five DST execution units.

FIG. 32 is a diagram of an example of distributed storage and task (DST)allocation information 242 for the example of FIG. 30. The DSTallocation information 242 includes data partitioning information 320,task execution information 322, and intermediate result information 324.The data partitioning information 320 includes the data identifier (ID),the number of partitions to split the data into, address information foreach data partition, and whether the DS encoded data has to betransformed from pillar grouping to slice grouping. The task executioninformation 322 includes tabular information having a taskidentification field 326, a task ordering field 328, a data partitionfield ID 330, and a set of DT execution modules 332 to use for thedistributed task processing per data partition. The intermediate resultinformation 324 includes tabular information having a name ID field 334,an ID of the DST execution unit assigned to process the correspondingintermediate result 336, a scratch pad storage field 338, and anintermediate result storage field 340.

Continuing with the example of FIG. 30, where tasks 1-3 are to bedistributedly performed on data 2, the data partitioning informationincludes the ID of data 2. In addition, the task distribution moduledetermines whether the DS encoded data 2 is in the proper format fordistributed computing (e.g., was stored as slice groupings). If not, thetask distribution module indicates that the DS encoded data 2 formatneeds to be changed from the pillar grouping format to the slicegrouping format, which will be done the by DSTN module. In addition, thetask distribution module determines the number of partitions to dividethe data into (e.g., 2_(—)1 through 2_z) and addressing information foreach partition.

The task distribution module generates an entry in the task executioninformation section for each sub-task to be performed. For example, task1_(—)1 (e.g., identify non-words on the data) has no task ordering(i.e., is independent of the results of other sub-tasks), is to beperformed on data partitions 2_(—)1 through 2_z by DT execution modules1_(—)1, 2_(—)1, 3_(—)1, 4_(—)1, and 5_(—)1. For instance, DT executionmodules 1_(—)1, 2_(—)1, 3_(—)1, 4_(—)1, and 5_(—)1 search for non-wordsin data partitions 2_(—)1 through 2_z to produce task 1_(—)1intermediate results (R1-1, which is a list of non-words). Task 1_(—)2(e.g., identify unique words) has similar task execution information astask 1_(—)1 to produce task 1_(—)2 intermediate results (R1-2, which isthe list of unique words).

Task 1_(—)3 (e.g., translate) includes task execution information asbeing non-ordered (i.e., is independent), having DT execution modules1_(—)1, 2_(—)1, 3_(—)1, 4_(—)1, and 5_(—)1 translate data partitions2_(—)1 through 2_(—)4 and having DT execution modules 1_(—)2, 2_(—)2,3_(—)2, 4_(—)2, and 5_(—)2—translate data partitions 2_(—)5 through 2_zto produce task 1_(—)3 intermediate results (R1-3, which is thetranslated data). In this example, the data partitions are grouped,where different sets of DT execution modules perform a distributedsub-task (or task) on each data partition group, which allows forfurther parallel processing.

Task 1_(—)4 (e.g., translate back) is ordered after task 1_(—)3 and isto be executed on task 1_(—)3's intermediate result (e.g., R1-3_(—)1)(e.g., the translated data). DT execution modules 1_(—)1, 2_(—)1,3_(—)1, 4_(—)1, and 5_(—)1 are allocated to translate back task 1_(—)3intermediate result partitions R1-3_(—)1 through R1-3_(—)4 and DTexecution modules 1_(—)2, 2_(—)2, 6_(—)1, 7_(—)1, and 7_(—)2 areallocated to translate back task 1_(—)3 intermediate result partitionsR1-3_(—)5 through R1-3_z to produce task 1-4 intermediate results (R1-4,which is the translated back data). Task 1_(—)5 (e.g., compare data andtranslated data to identify translation errors) is ordered after task1_(—)4 and is to be executed on task 1_(—)4's intermediate results(R4-1) and on the data. DT execution modules 1_(—)1, 2_(—)1, 3_(—)1,4_(—)1, and 5_(—)1 are allocated to compare the data partitions (2_(—)1through 2_z) with partitions of task 1-4 intermediate results partitionsR1-4_(—)1 through R1-4_z to produce task 1_(—)5 intermediate results(R1-5, which is the list words translated incorrectly).

Task 1_(—)6 (e.g., determine non-word translation errors) is orderedafter tasks 1_(—)1 and 1_(—)5 and is to be executed on tasks 1_(—)1'sand 1_(—)5's intermediate results (R1-1 and R1-5). DT execution modules1_(—)1, 2_(—)1, 3_(—)1, 4_(—)1, and 5_(—)1 are allocated to compare thepartitions of task 1_(—)1 intermediate results (R1-1_(—)1 throughR1-1_z) with partitions of task 1-5 intermediate results partitions(R1-5_(—)1 through R1-5_z) to produce task 1_(—)6 intermediate results(R1-6, which is the list translation errors due to non-words).

Task 1_(—)7 (e.g., determine words correctly translated) is orderedafter tasks 1_(—)2 and 1_(—)5 and is to be executed on tasks 1_(—)2'sand 1_(—)5's intermediate results (R1-1 and R1-5). DT execution modules1_(—)2, 2_(—)2, 3_(—)2, 4_(—)2, and 5_(—)2 are allocated to compare thepartitions of task 1_(—)2 intermediate results (R1-2_(—)1 throughR1-2_z) with partitions of task 1-5 intermediate results partitions(R1-5_(—)1 through R1-5_z) to produce task 1_(—)7 intermediate results(R1-7, which is the list of correctly translated words).

Task 2 (e.g., find specific words and/or phrases) has no task ordering(i.e., is independent of the results of other sub-tasks), is to beperformed on data partitions 2_(—)1 through 2_z by DT execution modules3_(—)1, 4_(—)1, 5_(—)1, 6_(—)1, and 7_(—)1. For instance, DT executionmodules 3_(—)1, 4_(—)1, 5_(—)1, 6_(—)1, and 7_(—)1 search for specificwords and/or phrases in data partitions 2_(—)1 through 2_z to producetask 2 intermediate results (R2, which is a list of specific wordsand/or phrases).

Task 3_(—)2 (e.g., find specific translated words and/or phrases) isordered after task 1_(—)3 (e.g., translate) is to be performed onpartitions R1-3_(—)1 through R1-3_z by DT execution modules 1_(—)2,2_(—)2, 3_(—)2, 4_(—)2, and 5_(—)2. For instance, DT execution modules1_(—)2, 2_(—)2, 3_(—)2, 4_(—)2, and 5_(—)2 search for specifictranslated words and/or phrases in the partitions of the translated data(R1-3_(—)1 through R1-3_z) to produce task 3_(—)2 intermediate results(R3-2, which is a list of specific translated words and/or phrases).

For each task, the intermediate result information indicates which DSTunit is responsible for overseeing execution of the task and, if needed,processing the partial results generated by the set of allocated DTexecution units. In addition, the intermediate result informationindicates a scratch pad memory for the task and where the correspondingintermediate results are to be stored. For example, for intermediateresult R1-1 (the intermediate result of task 1_(—)1), DST unit 1 isresponsible for overseeing execution of the task 1_(—)1 and coordinatesstorage of the intermediate result as encoded intermediate result slicesstored in memory of DST execution units 1-5. In general, the scratch padis for storing non-DS encoded intermediate results and the intermediateresult storage is for storing DS encoded intermediate results.

FIGS. 33-38 are schematic block diagrams of the distributed storage andtask network (DSTN) module performing the example of FIG. 30. In FIG.33, the DSTN module accesses the data 92 and partitions it into aplurality of partitions 1-z in accordance with distributed storage andtask network (DST) allocation information. For each data partition, theDSTN identifies a set of its DT (distributed task) execution modules 90to perform the task (e.g., identify non-words (i.e., not in a referencedictionary) within the data partition) in accordance with the DSTallocation information. From data partition to data partition, the setof DT execution modules 90 may be the same, different, or a combinationthereof (e.g., some data partitions use the same set while other datapartitions use different sets).

For the first data partition, the first set of DT execution modules(e.g., 1_(—)1, 2_(—)1, 3_(—)1, 4_(—)1, and 5_(—)1 per the DST allocationinformation of FIG. 32) executes task 1_(—)1 to produce a first partialresult 102 of non-words found in the first data partition. The secondset of DT execution modules (e.g., 1_(—)1, 2_(—)1, 3_(—)1, 4_(—)1, and5_(—)1 per the DST allocation information of FIG. 32) executes task1_(—)1 to produce a second partial result 102 of non-words found in thesecond data partition. The sets of DT execution modules (as per the DSTallocation information) perform task 1_(—)1 on the data partitions untilthe “z” set of DT execution modules performs task 1_(—)1 on the “zth”data partition to produce a “zth” partial result 102 of non-words foundin the “zth” data partition.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results toproduce the first intermediate result (R1-1), which is a list ofnon-words found in the data. For instance, each set of DT executionmodules 90 stores its respective partial result in the scratchpad memoryof DST execution unit 1 (which is identified in the DST allocation ormay be determined by DST execution unit 1). A processing module of DSTexecution 1 is engaged to aggregate the first through “zth” partialresults to produce the first intermediate result (e.g., R1_(—)1). Theprocessing module stores the first intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the first intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of non-words is of a sufficient size to partition(e.g., greater than a Terra-Byte). If yes, it partitions the firstintermediate result (R1-1) into a plurality of partitions (e.g.,R1-1_(—)1 through R1-1_m). If the first intermediate result is not ofsufficient size to partition, it is not partitioned.

For each partition of the first intermediate result, or for the firstintermediate result, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 34, the DSTN module is performing task 1_(—)2 (e.g., find uniquewords) on the data 92. To begin, the DSTN module accesses the data 92and partitions it into a plurality of partitions 1-z in accordance withthe DST allocation information or it may use the data partitions of task1_(—)1 if the partitioning is the same. For each data partition, theDSTN identifies a set of its DT execution modules to perform task 1_(—)2in accordance with the DST allocation information. From data partitionto data partition, the set of DT execution modules may be the same,different, or a combination thereof. For the data partitions, theallocated set of DT execution modules executes task 1_(—)2 to produce apartial results (e.g., 1^(st) through “zth”) of unique words found inthe data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results102 of task 1_(—)2 to produce the second intermediate result (R1-2),which is a list of unique words found in the data 92. The processingmodule of DST execution 1 is engaged to aggregate the first through“zth” partial results of unique words to produce the second intermediateresult. The processing module stores the second intermediate result asnon-DS error encoded data in the scratchpad memory or in another sectionof memory of DST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the second intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of unique words is of a sufficient size to partition(e.g., greater than a Terra-Byte). If yes, it partitions the secondintermediate result (R1-2) into a plurality of partitions (e.g.,R1-2_(—)1 through R1-2_m). If the second intermediate result is not ofsufficient size to partition, it is not partitioned.

For each partition of the second intermediate result, or for the secondintermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 35, the DSTN module is performing task 1_(—)3 (e.g., translate)on the data 92. To begin, the DSTN module accesses the data 92 andpartitions it into a plurality of partitions 1-z in accordance with theDST allocation information or it may use the data partitions of task1_(—)1 if the partitioning is the same. For each data partition, theDSTN identifies a set of its DT execution modules to perform task 1_(—)3in accordance with the DST allocation information (e.g., DT executionmodules 1_(—)1, 2_(—)1, 3_(—)1, 4_(—)1, and 5_(—)1 translate datapartitions 2_(—)1 through 2_(—)4 and DT execution modules 1_(—)2,2_(—)2, 3_(—)2, 4_(—)2, and 5_(—)2 translate data partitions 2_(—)5through 2_z). For the data partitions, the allocated set of DT executionmodules 90 executes task 1_(—)3 to produce partial results 102 (e.g.,1^(st) through “zth”) of translated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 1_(—)3 to produce the third intermediate result (R1-3), which istranslated data. The processing module of DST execution 2 is engaged toaggregate the first through “zth” partial results of translated data toproduce the third intermediate result. The processing module stores thethird intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the third intermediate result (e.g., translateddata). To begin the encoding, the DST client module partitions the thirdintermediate result (R1-3) into a plurality of partitions (e.g.,R1-3_(—)1 through R1-3_y). For each partition of the third intermediateresult, the DST client module uses the DS error encoding parameters ofthe data (e.g., DS parameters of data 2, which includes 3/5 decodethreshold/pillar width ratio) to produce slice groupings. The slicegroupings are stored in the intermediate result memory (e.g., allocatedmemory in the memories of DST execution units 2-6 per the DST allocationinformation).

As is further shown in FIG. 35, the DSTN module is performing task1_(—)4 (e.g., retranslate) on the translated data of the thirdintermediate result. To begin, the DSTN module accesses the translateddata (from the scratchpad memory or from the intermediate result memoryand decodes it) and partitions it into a plurality of partitions inaccordance with the DST allocation information. For each partition ofthe third intermediate result, the DSTN identifies a set of its DTexecution modules 90 to perform task 1_(—)4 in accordance with the DSTallocation information (e.g., DT execution modules 1_(—)1, 2_(—)1,3_(—)1, 4_(—)1, and 5_(—)1 are allocated to translate back partitionsR1-3_(—)1 through R1-3_(—)4 and DT execution modules 1_(—)2, 2_(—)2,6_(—)1, 7_(—)1, and 7_(—)2 are allocated to translate back partitionsR1-3_(—)5 through R1-3_z). For the partitions, the allocated set of DTexecution modules executes task 1_(—)4 to produce partial results 102(e.g., 1st through “zth”) of re-translated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_(—)4 to produce the fourth intermediate result (R1-4), which isretranslated data. The processing module of DST execution 3 is engagedto aggregate the first through “zth” partial results of retranslateddata to produce the fourth intermediate result. The processing modulestores the fourth intermediate result as non-DS error encoded data inthe scratchpad memory or in another section of memory of DST executionunit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the fourth intermediate result (e.g., retranslateddata). To begin the encoding, the DST client module partitions thefourth intermediate result (R1-4) into a plurality of partitions (e.g.,R1-4_(—)1 through R1-4_z). For each partition of the fourth intermediateresult, the DST client module uses the DS error encoding parameters ofthe data (e.g., DS parameters of data 2, which includes 3/5 decodethreshold/pillar width ratio) to produce slice groupings. The slicegroupings are stored in the intermediate result memory (e.g., allocatedmemory in the memories of DST execution units 3-7 per the DST allocationinformation).

In FIG. 36, a distributed storage and task network (DSTN) module isperforming task 1_(—)5 (e.g., compare) on data 92 and retranslated dataof FIG. 35. To begin, the DSTN module accesses the data 92 andpartitions it into a plurality of partitions in accordance with the DSTallocation information or it may use the data partitions of task 1_(—)1if the partitioning is the same. The DSTN module also accesses theretranslated data from the scratchpad memory, or from the intermediateresult memory and decodes it, and partitions it into a plurality ofpartitions in accordance with the DST allocation information. The numberof partitions of the retranslated data corresponds to the number ofpartitions of the data.

For each pair of partitions (e.g., data partition 1 and retranslateddata partition 1), the DSTN identifies a set of its DT execution modules90 to perform task 1_(—)5 in accordance with the DST allocationinformation (e.g., DT execution modules 1_(—)1, 2_(—)1, 3_(—)1, 4_(—)1,and 5_(—)1). For each pair of partitions, the allocated set of DTexecution modules executes task 1_(—)5 to produce partial results 102(e.g., 1^(st) through “zth”) of a list of incorrectly translated wordsand/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results oftask 1_(—)5 to produce the fifth intermediate result (R1-5), which isthe list of incorrectly translated words and/or phrases. In particular,the processing module of DST execution 1 is engaged to aggregate thefirst through “zth” partial results of the list of incorrectlytranslated words and/or phrases to produce the fifth intermediateresult. The processing module stores the fifth intermediate result asnon-DS error encoded data in the scratchpad memory or in another sectionof memory of DST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the fifth intermediate result. To begin theencoding, the DST client module partitions the fifth intermediate result(R1-5) into a plurality of partitions (e.g., R1-5_(—)1 through R1-5_z).For each partition of the fifth intermediate result, the DST clientmodule uses the DS error encoding parameters of the data (e.g., DSparameters of data 2, which includes 3/5 decode threshold/pillar widthratio) to produce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 1-5 per the DST allocation information).

As is further shown in FIG. 36, the DSTN module is performing task1_(—)6 (e.g., translation errors due to non-words) on the list ofincorrectly translated words and/or phrases (e.g., the fifthintermediate result R1-5) and the list of non-words (e.g., the firstintermediate result R1-1). To begin, the DSTN module accesses the listsand partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-1_(—)1 and partitionR1-5_(—)1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_(—)6 in accordance with the DST allocation information(e.g., DT execution modules 1_(—)1, 2_(—)1, 3_(—)1, 4_(—)1, and 5_(—)1).For each pair of partitions, the allocated set of DT execution modulesexecutes task 1_(—)6 to produce partial results 102 (e.g., 1^(st)through “zth”) of a list of incorrectly translated words and/or phrasesdue to non-words.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 1_(—)6 to produce the sixth intermediate result (R1-6), which isthe list of incorrectly translated words and/or phrases due tonon-words. In particular, the processing module of DST execution 2 isengaged to aggregate the first through “zth” partial results of the listof incorrectly translated words and/or phrases due to non-words toproduce the sixth intermediate result. The processing module stores thesixth intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the sixth intermediate result. To begin theencoding, the DST client module partitions the sixth intermediate result(R1-6) into a plurality of partitions (e.g., R1-6_(—)1 through R1-6_z).For each partition of the sixth intermediate result, the DST clientmodule uses the DS error encoding parameters of the data (e.g., DSparameters of data 2, which includes 3/5 decode threshold/pillar widthratio) to produce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 2-6 per the DST allocation information).

As is still further shown in FIG. 36, the DSTN module is performing task1_(—)7 (e.g., correctly translated words and/or phrases) on the list ofincorrectly translated words and/or phrases (e.g., the fifthintermediate result R1-5) and the list of unique words (e.g., the secondintermediate result R1-2). To begin, the DSTN module accesses the listsand partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-2_(—)1 and partitionR1-5_(—)1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_(—)7 in accordance with the DST allocation information(e.g., DT execution modules 1_(—)2, 2_(—)2, 3_(—)2, 4_(—)2, and 5_(—)2).For each pair of partitions, the allocated set of DT execution modulesexecutes task 1_(—)7 to produce partial results 102 (e.g., 1^(st)through “zth”) of a list of correctly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_(—)7 to produce the seventh intermediate result (R1-7), which isthe list of correctly translated words and/or phrases. In particular,the processing module of DST execution 3 is engaged to aggregate thefirst through “zth” partial results of the list of correctly translatedwords and/or phrases to produce the seventh intermediate result. Theprocessing module stores the seventh intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the seventh intermediate result. To begin theencoding, the DST client module partitions the seventh intermediateresult (R1-7) into a plurality of partitions (e.g., R1-7_(—)1 throughR1-7_z). For each partition of the seventh intermediate result, the DSTclient module uses the DS error encoding parameters of the data (e.g.,DS parameters of data 2, which includes 3/5 decode threshold/pillarwidth ratio) to produce slice groupings. The slice groupings are storedin the intermediate result memory (e.g., allocated memory in thememories of DST execution units 3-7 per the DST allocation information).

In FIG. 37, the distributed storage and task network (DSTN) module isperforming task 2 (e.g., find specific words and/or phrases) on the data92. To begin, the DSTN module accesses the data and partitions it into aplurality of partitions 1-z in accordance with the DST allocationinformation or it may use the data partitions of task 1_(—)1 if thepartitioning is the same. For each data partition, the DSTN identifies aset of its DT execution modules 90 to perform task 2 in accordance withthe DST allocation information. From data partition to data partition,the set of DT execution modules may be the same, different, or acombination thereof. For the data partitions, the allocated set of DTexecution modules executes task 2 to produce partial results 102 (e.g.,1st through “zth”) of specific words and/or phrases found in the datapartitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 7 is assigned to process the first through “zth” partial results oftask 2 to produce task 2 intermediate result (R2), which is a list ofspecific words and/or phrases found in the data. The processing moduleof DST execution 7 is engaged to aggregate the first through “zth”partial results of specific words and/or phrases to produce the task 2intermediate result. The processing module stores the task 2intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 7.

DST execution unit 7 engages its DST client module to slice groupingbased DS error encode the task 2 intermediate result. To begin theencoding, the DST client module determines whether the list of specificwords and/or phrases is of a sufficient size to partition (e.g., greaterthan a Terra-Byte). If yes, it partitions the task 2 intermediate result(R2) into a plurality of partitions (e.g., R2_(—)1 through R2_m). If thetask 2 intermediate result is not of sufficient size to partition, it isnot partitioned.

For each partition of the task 2 intermediate result, or for the task 2intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, and 7).

In FIG. 38, the distributed storage and task network (DSTN) module isperforming task 3 (e.g., find specific translated words and/or phrases)on the translated data (R1-3). To begin, the DSTN module accesses thetranslated data (from the scratchpad memory or from the intermediateresult memory and decodes it) and partitions it into a plurality ofpartitions in accordance with the DST allocation information. For eachpartition, the DSTN identifies a set of its DT execution modules toperform task 3 in accordance with the DST allocation information. Frompartition to partition, the set of DT execution modules may be the same,different, or a combination thereof. For the partitions, the allocatedset of DT execution modules 90 executes task 3 to produce partialresults 102 (e.g., 1^(st) through “zth”) of specific translated wordsand/or phrases found in the data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 5 is assigned to process the first through “zth” partial results oftask 3 to produce task 3 intermediate result (R3), which is a list ofspecific translated words and/or phrases found in the translated data.In particular, the processing module of DST execution 5 is engaged toaggregate the first through “zth” partial results of specific translatedwords and/or phrases to produce the task 3 intermediate result. Theprocessing module stores the task 3 intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 7.

DST execution unit 5 engages its DST client module to slice groupingbased DS error encode the task 3 intermediate result. To begin theencoding, the DST client module determines whether the list of specifictranslated words and/or phrases is of a sufficient size to partition(e.g., greater than a Terra-Byte). If yes, it partitions the task 3intermediate result (R3) into a plurality of partitions (e.g., R3_(—)1through R3_m). If the task 3 intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the task 3 intermediate result, or for the task 3intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, 5, and 7).

FIG. 39 is a diagram of an example of combining result information intofinal results 104 for the example of FIG. 30. In this example, theresult information includes the list of specific words and/or phrasesfound in the data (task 2 intermediate result), the list of specifictranslated words and/or phrases found in the data (task 3 intermediateresult), the list of non-words found in the data (task 1 firstintermediate result R1-1), the list of unique words found in the data(task 1 second intermediate result R1-2), the list of translation errorsdue to non-words (task 1 sixth intermediate result R1-6), and the listof correctly translated words and/or phrases (task 1 seventhintermediate result R1-7). The task distribution module provides theresult information to the requesting DST client module as the results104.

FIG. 40 is a diagram of an embodiment of a structure of a region headerobject 350 that includes a visible regions section 352, an open writetransactions section 354, and an open delete transactions section 356.The structure of the region header object is utilized to generate andupdate the region header object 350. The region header object 350 isassociated with a data object that is stored as a plurality of encodeddata slices in a dispersed storage network (DSN). The region headerobject 350 may be utilized to identify a storage location of the dataobject within the DSN. The data object may be stored as one or moreregions within the DSN where each region includes at least one datasegment. Each data segment of the plurality of data segments is encodedusing a dispersed storage error coding function to produce a set ofencoded data slices of the plurality of sets of encoded data slices. Forexample, a first grouping of sets of encoded data slices is producedcorresponding to a first region and a second grouping of sets of encodeddata slices is produced corresponding to a second region. Each region ofthe one or more regions is stored in the DSN at a storage locationcorresponding to a dispersed storage (DS) address for the region. Eachregion may be uniquely identified by a region identifier (ID) 360. Theregion header object 350 is stored in the DSN as a set of header slicesat a storage location that includes a region header object DS address.At least one of a directory and an index may be utilized identify theregion header object DS address based on an object name (e.g., data ID)for the data object.

The visible regions section 352 identifies visible regions, if any, by aregion entry for each region associated with the data object. A regionis visible when each data segment of the one or more data segmentsassociated with the region includes at least a write threshold number offavorably committed encoded data slices stored in the DSN. A favorablycommitted encoded data slice is visible (e.g., available) for retrievingwhen the encoded data slice has been written to the DSN and isassociated with a favorably executed commit request.

A region entry of the visible regions section 352 includes, for eachvisible region, a region ID field 360, a DS address field 362, a regionsize field 364, and a segment size field 366. A region ID entry isincluded in the region ID field 360 to uniquely identify the region. ADS address entry is included in the DS address field 362 to identify astorage location of a first data segment of the one or more datasegments associated with the region. For example, the DS address entryidentifies a source name for a first data segment. Source namescorresponding to other data segments of the one or more data segments ofthe region may be generated based on the source name for the first datasegment (e.g., incrementing a segment field entry by one for eachsequential data segment of the one or more data segments of the region).A region size entry of the region size field 364 indicates a size of theregion (e.g., bytes). A segment size entry of the segment size field 366indicates a size (e.g., bytes) of each data segment of the one or moredata segments of the region. A number of data segments of the one ormore data segments of the region may be determined by dividing theregion size entry by the segment size entry.

The open write transactions section 354 identifies open writetransactions with regards to the data object. An open write transactionincludes a write transaction that is in progress for a region but hasnot yet produced visibility of the region. The open write transactionsection 354 includes a subsection for each, if any, transaction that isassociated with at least one region of an open write transaction. Eachsubsection of the open write transaction section includes a transactionidentifier (ID) 358 and a region entry for each region associated withthe open write transaction.

The open delete transactions section 356 identifies open deletetransactions with regards to the data object. An open delete transactionincludes a delete transaction that is in progress for a region but hasnot yet produced full deletion of the region. The open deletetransaction section 356 includes a subsection for each, if any,transaction that is associated with at least one region of an opendelete transaction. Each subsection of the open delete transactionsection 356 includes a transaction identifier (ID) 358 and a regionentry for each region associated with the open delete transaction.

FIG. 41 is a flowchart illustrating an example of reading a data object.The method begins at step 370 where a processing module (e.g., of adispersed storage (DS) processing module) receives a read data objectrequest that includes an object identifier (ID). The request may alsoinclude an offset, where the offset indicates a position within the dataobject for initiating retrieval. The method continues at step 372 wherethe processing module identifies a dispersed storage (DS) addressassociated with the object ID. The identifying includes at least one ofa directory lookup using the object ID, an index lookup using the objectID, and extracting the DS address from the read data object request. Themethod continues at step 374 where the processing module identifies astarting offset within a data object of the read data object request.The identifying includes at least one of extracting the starting offsetfrom the read data object request and establishing a zero offset whennot receiving the offset.

The method continues at step 376 where the processing module retrieves aregion header object from a dispersed storage network (DSN) memory usingthe DS address. The retrieving includes generating a set of header slicenames using the DS address, generating a set of read slice requests thatincludes the set of header slice names, outputting the set of read slicerequests to the DSN memory, receiving at least a decode threshold numberof header slices, and decoding the at least a decode threshold number ofheader slices to produce the region header object.

The method continues at step 378 where the processing module identifiesa starting region of one or more visible regions of the region headerobject based on the starting offset. For example, the processing moduleadds region sizes of the one or more visible regions until a startingregion is identified that does not exceed the starting offset. Themethod continues at step 380 where the processing module identifies astarting segment of the starting region based on the starting offset.The identifying includes a series of steps. A first step includesdividing a difference between the starting offset and the startingregion by a segment size of the region to produce a segment number. Asecond step includes adding the segment number to a DS address of theregion to produce a DS address of the starting segment.

The method continues at step 382 where the processing module retrievesthe starting segment and any other subsequent segments of the startingregion from the DSN memory. The retrieving includes a series of steps. Afirst step includes generating a set of retrieve slice requests for thestarting segment using the DS address of the starting segment. A secondstep includes outputting the set of retrieve slice requests to the DSNmemory. A third step includes determining a remaining number of datasegments of the starting region based on segment size, region size, andthe segment number of the starting segment. A fourth step includes, foreach remaining data segment, generating another set of retrieve slicerequests using the DS address of the starting segment and a segmentnumber of the remaining data segment. A fifth step includes outputtingthe other set of retrieve slice requests to the DSN memory. A sixth stepincludes receiving at least a decode threshold number of encoded dataslices for each of the starting data segment and the remaining datasegments. A seventh step includes decoding the at least a decodethreshold number of encoded data slices for each of the starting datasegment and the remaining data segments using the dispersed storageerror coding function to reproduce the starting data segment and theremaining data segments.

The method continues at step 384 where the processing module retrievesall segments of any subsequent visible regions to the starting regionfrom the DSN memory. For example, for each region entry of the one ormore visible regions subsequent to the starting region, the processingmodule identifies a DS address of the region, determines a number ofdata segments based on a region size and a segment size, generates a setof slice names for each data segment (e.g., sequentially incrementing adata segment ID for each sequential data segment), generates retrieveslice requests for each set of slice names, outputs the retrieve slicerequests, receives at least a decode threshold number of encoded dataslices for each data segment, and decodes the at least a decodethreshold number of encoded data slices for each data segment toreproduce each data segment of the region.

The method continues at step 386 where the processing module compilesretrieved data segments to reproduce the data object. The compilingincludes aggregating all retrieve segments in order to reproduce thedata. In addition, the processing module may include DS addresses ofeach region. The method continues at step 388 where the processingmodule outputs the data object to a requesting entity.

FIG. 42 is a flowchart illustrating an example of writing a data object.The method begins at step 390 where a processing module (e.g., of adispersed storage (DS) processing module) receives a write data objectrequest that includes a data object for storage. The request may furtherinclude a data object identifier (ID) of the data object. The methodcontinues at step 392 where the processing module generates a regionheader object to include one open write transaction and no regionentries. The generating includes generating a transaction ID andgenerating an open write transaction section to include the transactionID. The region header object is generated in accordance with a structureof the region header object depicted in FIG. 40.

The method continues at step 394 where the processing module stores theregion header object in a dispersed storage network (DSN) memory. Thestoring includes a series of steps. A first step includes generating adispersed storage (DS) address as a storage location for storing theregion header object in the DSN memory. A second step includes encodingthe region header object using a dispersed storage error coding functionto produce a set of header slices. A third step includes generating aset of header slice names associated with the set of header slices usingthe DS address. A fourth step includes generating a set of write slicerequests that includes the set of header slices and the set of headerslice names. A fifth step includes outputting the set of write slicerequests to the DSN memory. A six step includes receiving write sliceresponses from the DSN memory. A seventh step includes generating andoutputting a set of commit requests that includes a transaction numberof the set of write slice requests to the DSN memory when a writethreshold number of favorable write slice responses has been received.

The method continues at step 396 where the processing module stores afirst region of the data object in the DSN memory. The storing includesa series of steps. A first step includes determining whether a dataobject size is less than a region size threshold (e.g., a region size asa maximum). When the data object size is less than or equal to theregion size threshold, a second step includes segmenting the data objectin accordance with a segment size to produce a plurality of segments ofthe first region. Alternatively, when the data object size is greaterthan the region size threshold, the second step includes segmenting afirst region size portion of the data object in accordance with thesegment size to produce the plurality of data segments of the firstregion.

A third step includes encoding each of the plurality of data segments ofthe first region using the dispersed storage error coding function toproduce a plurality of sets of encoded data slices of the first region.A fourth step includes generating a first segment DS address as astorage location for storing the first segment of the plurality of datasegments of the first region in the DSN memory. A fifth step includesgenerating a plurality of sets of data slice names associated with theplurality of sets of encoded data slices using the first segment DSaddress. A sixth step includes generating at least one set of writeslice requests that includes the plurality of sets of data slice namesand the plurality of sets of encoded data slices. A seventh stepincludes outputting the at least one set of write slice requests to theDSN memory. An eighth step includes receiving write slice responses fromthe DSN memory with regards to the at least one set of write slicerequests. An ninth step includes generating and outputting a pluralityof sets of commit requests that includes a transaction number associatedwith the at least one set of write slice requests to the DSN memory whena write threshold number of favorable write slice responses has beenreceived for each of the plurality of sets of encoded data slices.

The method continues at step 398 where the processing module updates theopen write transaction section of the region header object in the DSNmemory with regards to storing the first region. The updating includes aseries of steps. A first step includes generating a region entry for thefirst region. For example, the processing module generates the regionentry to include a region identifier, the first segment DS address, theregion size, and the segment size. A second step includes updating theopen write transaction section to include the region entry for the firstregion. When the data object size is greater than the region size, athird step includes generating a region entry for a second region toinclude a region identifier for the second region and updating the openwrite transaction section to include the region entry for the secondregion. A fourth step includes storing the updated region header objectin the DSN memory (e.g., encode, write slices, commit slices aspreviously discussed).

The method continues at step 400 where the processing module stores anysubsequent regions of the data object in the DSN memory in a fashion aspreviously discussed. For each subsequent region of the data object, themethod continues at step 402 where the processing module updates theregion header object in the DSN memory for the subsequent region in afashion as previously discussed.

When each segment of each region has been committed, the methodcontinues at step 404 where the processing module updates the regionheader object by transferring region entries of the open writetransaction section to the visible regions section. For example, theprocessing module transfers the region entry for the first region fromthe open write transaction section (e.g., for the transaction ID) to thevisible regions section. Next, the processing module transfers anyremaining region entries of the open write transaction section (e.g.,for the transaction ID) to the visible regions section. When each regionentry associated with the transaction ID has been transferred from theopen write transaction section to the visible regions section, theprocessing module deletes the transaction ID from the open transactionsection. The method continues at step 406 where the processing modulestores the updated region header object in the DSN memory.

FIG. 43A is a diagram of an embodiment of a structure of a hierarchicalregion header object that includes a region header object 350 and one ormore sub-region header objects 408. The region header object 350includes one or more visible region entries 352 associated with storageof one or more data objects stored by data segments in a dispersedstorage network (DSN), one or more sub-region entries associated withthe one or more sub-region header objects 408, and may include openwrite transaction entries and open delete transaction entries (e.g., asillustrated in FIG. 40). Each sub-region header object 408 includes oneor more visible region entries 352 associated with storage of one ormore data objects stored by data segments in the DSN associated with acorresponding sub-region and may include open write transaction entriesand open delete transaction entries.

A visible region entry of the one or more visible regions entries 352includes, for each visible region, a region identifier (ID) field 360, adispersed storage (DS) address field 362, a region size field 364, and asegment size field 366. A region ID entry is included in the region IDfield 360 to uniquely identify the region. A DS address entry isincluded in the DS address field 362 to identify a storage location of afirst data segment of the one or more data segments associated with theregion. For example, the DS address entry identifies a source name for afirst data segment. Source names corresponding to other data segments ofone or more data segments of the region may be generated based on thesource name for the first data segment (e.g., incrementing a segmentfield entry by one for each sequential data segment of the one or moredata segments of the region). A region size entry of the region sizefield 364 indicates a size of the region (e.g., bytes). A segment sizeentry of the segment size field 366 indicates a size (e.g., bytes) ofeach data segment of the one or more data segments of the region. Anumber of data segments of the one or more data segments of the regionmay be determined by dividing the region size entry by the segment sizeentry.

A sub-region entry of the one or more sub-region entries 408 includes,for each sub-region, a region ID field 360 of the sub-region, a DSaddress field 362 of the sub-region header object, and a region sizefield 364 of the sub-region. A region ID entry is included in the regionID field 360 to uniquely identify the sub-region. A DS address entry isincluded in the DS address field 362 to identify a storage location thesub-region header object 408 associated with the sub-region. Forexample, the DS address entry identifies a source name for thesub-region header object 408. A region size entry of the region sizefield 364 indicates a size of the sub-region (e.g., bytes). The size ofthe sub-region may include one or more of a size of the one or morevisible regions, a size of one or more regions associated with openwrite transactions, and a size of one or more regions associated withopen delete transactions. The method of operation utilizing thehierarchical region header object structure is discussed in greaterdetail with reference to FIG. 43B.

FIG. 43B is a flowchart illustrating another example of reading a dataobject, which includes similar steps to FIG. 41. The method begins withsteps 370, 372, 374, 376, and 378 of FIG. 41 where a processing module(e.g., a dispersed storage (DS) processing module) receives a read dataobject requests that includes an object identifier, identifies a DSaddress associated with the object identifier (e.g., for a top-levelregion header object), identifies a starting offset within a data objectof the read data object request, retrieves a region header object from adispersed storage network (DSN), and identifies a starting region of oneor more visible regions of the region header object based on thestarting offset.

The method continues at step 410 where the processing module determineswhether the starting region is a sub-region header object (e.g.,examining a corresponding entry type and/or whether a segment sizeexists). The method branches to step 380 of FIG. 41 when the processingmodule determines that the starting region is not a sub-region headerobject. The method continues to step 412 when the processing moduledetermines that the starting region is a sub-region header object. Themethod continues at step 412 where the processing module identifies a DSaddress associated with the starting region (e.g., by extracting the DSaddress from the region entry). The method branches back to step 376 ofFIG. 41 where the processing module retrieves the region header objectfrom the DSN.

When the processing module determines that the starting region is not asub-region header object, the method continues with steps 380 and 382 ofFIG. 41 where the processing module identifies a starting segment of thestarting region based on the starting offset and retrieves the startingsegment and any other subsequent segments of the starting region fromthe DSN memory. The method continues at step 414 where the processingmodule retrieves all segments of any subsequent visible region to thestarting region from the DSN memory for all sub-regions (e.g., traverseback up the hierarchy for each sub-region). The method continues withsteps 386 and 388 of FIG. 41 where the processing module compilesretrieve segments to produce data and outputs the data to a requestingentity.

FIG. 44 is a flowchart illustrating another example of writing a dataobject, which includes similar steps to FIG. 42. The method begins withstep 390 of FIG. 42 where a processing module (e.g., of a dispersedstorage processing module) receives a write data object requests thatincludes a data object for storage. The method continues at step 416where the processing module generates a deterministically determined setof slice names based on the request. For example, the processing moduleperforms a mask generating function on a data name of the data object toproduce an object identifier (ID) and utilizes the object identifier togenerate a vault source name common utilized to generate the set ofslice names.

The method continues at step 418 where the processing module outputs aset of write slice requests to a dispersed storage network (DSN) thatincludes the set of slice names and a transaction ID. Such writing mayintroduce a lock state for the data object while a set of commit writeslice requests are never sent to the DSN. The processing module maygenerate the transaction ID based on one or more of a random number anda previously utilized transaction ID. The outputting the set of writeslice requests to include generating the set of write slice requests toinclude null data in addition to the set of slice names and thetransaction ID. The method continues at step 420 where the processingmodule indicates that a write sequence is active pertaining to the dataobject. For example, the processing module sends a write sequence activeindicator to a cleanup process where the indicator includes the dataname of the data object and the transaction ID.

The method continues with steps 392-406 of FIG. 42 to store the dataobject in the DSN, where the processing module generates a region headerobject to include one open write transactions with the regions, storesthe region header object in the DSN memory, stores a first region of thedata object in the DSN memory, updates the region header object in theDSN memory, stores any subsequent regions of the data object in the DSNmemory, updates the region header object in the DSN memory forsubsequent regions, when each segment of the region has been committed,updates the region header object by transferring region entries of theopen write transactions to visible regions, and stores the updatedregion header object in the DSN memory. The method continues at step 422where the processing module outputs a set of rollback transactionrequests to the DSN memory that includes the transaction ID. Suchsending of the rollback transaction requests eliminates the slice namelock. The DS units of the DSN memory may timeout and perform anautomatic rollback when an expiration time period expires afterreceiving the initial write request and no commit write request has beenreceived. The method continues at step 424 where the processing moduleindicates that the write sequence is not active. For example, theprocessing module outputs a write sequence not active indicator to thecleanup process.

Alternatively, or in addition to the write sequence example, the cleanupprocess may attempt to write to the same set of slice names that providethe slice name lock. When the cleanup process receives a succeededindicator in response, the cleanup process indicates that the writesequence has failed (e.g., the automatic rollback of the DS units hastaken place since the write sequence has taken too long).

FIG. 45A is a schematic block diagram of an embodiment of a dispersedstorage system that includes a dispersed storage (DS) processing module426 and a DS unit set 428. The DS unit set 428 includes a set of DSunits 430 utilized to access slices stored in the set of DS units. EachDS unit 430 of the set of DS units may be implemented utilizing at leastone of a distributed storage and task (DST) execution unit, a storageserver, and one or more memory devices. The DS processing module 426 maybe implemented utilizing at least one of a DST client module, a DSTprocessing unit, a DS processing unit, a user device, a DST executionunit, and a DS unit. The system is operable to facilitate renaming of anamed object stored in the DS unit set.

In an example of operation, the DS processing module 426 receives arequest to rename the named object that includes a source object name ofthe named object and a destination object name. Alternatively, the DSprocessing module 426 receives the request to rename the named objectthat includes a data name associated with source data and a data nameassociated with destination data. When receiving one or more data names,the DS processing module 426 may perform a dispersed storage indexlookup to identify the source object name and the destination objectname based on the one or more data names. The identifying includesgenerating and outputting index slice requests 432 to the DS unit set428 to access the dispersed index, receiving index slice responses 434that includes a portion of the dispersed index, and decoding the indexslice responses to reproduce one or more of the source object name andthe destination object name.

The DS processing module 426 utilizes the source object name to retrievea source region header object from the DS unit set 428. The retrievingincludes generating and outputting data slice access requests 436 to theDS unit set 428 that includes slice names generated from the sourceobject name. The DS processing module 426 attempts to retrieve adestination region header object from the DS unit set 428 based on thedestination object name. The retrieving includes generating andoutputting data slice access requests 436 to the DS unit set 428 thatincludes slice names generated from the destination object name. Whenthe retrieving is not successful (e.g., as indicated by a receiving dataslice access responses 438 indicating an unfavorable result), the DSprocessing module 426 generates a new destination region header object.

The DS processing module 426 identifies visible region entries of thedestination region header object and moves the identified visible regionentries to open delete transactions of the destination region headerobject to facilitate deleting of data associated with the destinationobject name (e.g., deleting old data associated with the name to beutilized in the renaming). The DS processing module 426 identifiesvisible region entries of the source region header object and moves theidentified visible region entries to the visible region entries of thedestination region header object (e.g., data to keep that is beingrenamed). The DS processing module 426 deletes the source region headerobject from the DS unit set 428 when all open write transactions haveconcluded, visible regions have been moved, and all open deletetransactions have concluded. The deleting includes generating deletedata slice access requests 436 and receiving data slice access responses438 to confirm deletion of slice is associated with the source regionheader object. As such, the source region header object is no longerrequired.

The DS processing module 426 outputs write data slice access requests436 to the DS unit set 428 that includes the destination region headerobject. The DS processing module 426 updates the dispersed index toassociate the destination object name with the destination region headerobject (e.g., a dispersed index entry includes the destination objectname and a source name of the destination region header object). The DSprocessing module 426 updates the dispersed index to disassociate thesource object name with the source region header object (e.g., sourceobject name does not point to a region header object).

FIG. 45B is a flowchart illustrating an example of renaming a namedobject. The method begins with step 440 where a processing module (e.g.,a dispersed storage (DS) processing module) receives a request to renamea named object that includes a source object name of the named objectand a destination object name. The method continues at step 442 wherethe processing module retrieves a source region header object from adispersed storage network (DSN) based on the source object name. Theretrieving includes accessing a DSN index using the source object nameto identify a DSN address (e.g., a source name) of the source regionheader object, generating slice requests utilizing the DSN address(e.g., generate slice names to include the source name), outputting theslice requests to the DSN, receiving slices, and decoding the slices toreproduce the source region header object.

The method continues at step 444 where the processing module obtains adestination region header object from the DSN based on the destinationobject name. The retrieving includes accessing the DSN index using thedestination object name to identify a DSN address (e.g., a source name)of the destination region header object, generating slice requestsutilizing the DSN address (e.g., generate slice names to include thesource name), outputting the slice requests to the DSN, receivingslices, and decoding the slices to reproduce the destination regionheader object. Alternatively, the accessing of the DSN index may failwhen the destination region header object does not exist. The processingmodule generates a new destination region header object when thedestination region header object does not exist.

The method continues at step 446 where the processing module movesvisible region entries of the destination region header object to theopen delete transactions of the destination region header object. Theprocessing module may initiate a cleanup task to remove the olddestination content. The cleanup may further include the processingmodule waiting for open write transactions to become visible forinclusion in the moving of the visible region entries of the destinationregion header object to the open delete transactions of the destinationregion header object.

The method continues at step 448 where the processing module movesvisible region entries of the source region header object to the visibleregion entries of the destination region header object. The processingmodule may further wait for entries of open write transactions of thesource region header object to conclude (e.g., to become visible) to beincluded in the moving of the visible region entries of the sourceregion header object to the visible region entries of the destinationregion header object.

The method continues at step 450 where the processing module deletes thesource region header object from the DSN when open write transactionshave concluded, visible regions have been moved, and open deletetransactions have concluded. The deleting includes issuing delete writerequests to the DSN that includes slice names based on the DSN addressof the source region header object. The method continues at step 452where the processing module writes the destination region header objectto the DSN. The writing includes generating a set of slice names usingthe DSN address of the destination region header object or utilizing anew DSN address, encoding the destination region header object using adispersed storage error coding function to produce a set of headerslices, generating a set of write slice requests that includes the setof slice names and the set of header slices, and outputting the set ofwrite slice requests to the DSN.

The method continues at step 454 where the processing module updates aDSN index to associate the destination object name with the destinationregion header object. The updating may include obtaining an index keyassociated with the source object (e.g., generating based on analyzingdata of the source object, retrieving from an entry of the DSN indexassociated with the source object name), obtaining an entry of the DSNindex associated with the destination object name, updating the entrywith the index key, overwriting a DSN address with the DSN address ofthe destination region header object, encoding the entry using thedispersed storage error coding function to produce a set of indexslices, and outputting the set of index slices to the DSN. The methodcontinues at step 456 where the processing module updates the DSN indexto disassociate the source object name with the source region headerobject. For example, the processing module facilitates deletion of theDSN index associated with the source object name (e.g., issuing a set ofdelete write slice requests to the DSN).

FIG. 46A is a schematic block diagram of another embodiment of adispersed storage system that includes a dispersed storage (DS)processing module 426, a cleanup module 458, and a DS unit set 428. TheDS unit set 428 includes a set of DS units 430 utilized to access slicesstored in the set of DS units. Each DS unit 430 of the set of DS unitsmay be implemented utilizing at least one of a distributed storage andtask (DST) execution unit, a storage server, and one or more memorydevices. The DS processing module 426 and cleanup module 458 may beimplemented utilizing at least one of a DST client module, a DSTprocessing unit, a DS processing unit, a user device, a DST executionunit, and a DS unit. The system is operable to facilitate storing datain the DS unit set.

The DS processing module 426 generates and outputs a set of transientslices of a first revision for storage in the DS unit set, where the setof transient slices corresponds to a data object to be stored in the DSunit set. The generating includes generating a set of data slice accessrequests 436 that includes the set of transient slices. The outputtingincludes outputting the set of data slice access requests 436 to the DSunit set 428 in receiving data slice access responses 438 indicatingstatus of the storage request. The DS processing module 426 issues acleanup notification 460 to notify the cleanup module 458 that a storagesequence is active corresponding to the data object. Next, the DSprocessing module 426 facilitates storage of the data object in the DSunit set 428. While facilitating storage of the data object in the DSunit set 428 utilizing one or more storage regions and updating a regionheader object corresponding to the data object, the DS processing module426 outputs, at time intervals less than the minimum time intervalperiod, another set of transient slices of another revision for storagein the DS unit set 428.

When completing storage of the data object, the DS processing module 426deletes the set of transient slices by issuing a set of delete dataslice access requests 436 to the DS unit set 428 for the transientslices. The DS processing module 426 issues another cleanup notification460 to notify the cleanup module 458 that the storage sequence is notactive corresponding to the data object.

Each DS unit 430 of the DS unit set receives a data slice access requestthat includes a transient slice of the set of transit slices. The DSunit 430 stores the transient slice. When receiving another transientslice of another revision number (e.g., but same slice name) within anallowable timeframe from a last received revision, the DS unit 430stores the other transient slice and other revision number. Wheneverreceiving the other transient slice of the other revision number withinthe allowable timeframe, the DS unit 430 updates a slice status for thetransient slice indicating that the transient slice is visible.

The cleanup module 458 receives the cleanup notification 460 from the DSprocessing module 426 that the storage sequence is active correspondingto the data object. From time to time, the cleanup module 458 performs alist function for the set of transient slices corresponding to the dataobject. For example, the cleanup module generates a set of list dataslice access requests 436, outputs the set of list data slice accessrequests 436 to the DS unit set, and receives a set of list data sliceaccess responses 438 from the DS unit set 428. When a list functionresponse indicates that the transient slices are visible (e.g., since aperiodic refresh of the transient slices with a new revision number wasnot received by the DS units), the cleanup module 458 indicates astorage sequence error for the data object and may further initiate acleanup process. The method of operation is discussed in greater detailwith reference to FIG. 46B.

FIG. 46B is a flowchart illustrating another example of writing a dataobject, which includes similar steps to FIGS. 42 and 44. The methodbegins with step 390 of FIG. 42 where a processing module (e.g., of adispersed storage (DS) processing module) receives a write data objectrequests that includes a data object for storage. The method continuesat step 462 where the processing module generates a set of transientslices of a first revision corresponding to the data object. Thegenerating includes generating the set of transient slices to include atleast one of a predetermined pattern, a random pattern, and adeterministic value associated with the data object (e.g., applying adeterministic function to at least a portion of the data and/or a dataobject identifier) and generating a set of slice names based on the dataobject identifier. The method continues at step 464 where the processingmodule outputs the set of transient slices to a dispersed storagenetwork (DSN) memory. The outputting includes generating a set of dataslice access requests that includes a set of write transient slicerequests, the set of slice names, the set of transient slices, and thefirst revision. The method continues with step 420 of FIG. 44 where theprocessing module indicates that a write sequence is active.

The method continues at step 466 where the processing module initiatesfacilitation of storage of the data object in the DSN memory. Thefacilitation includes utilizing one or more storage regions and updatinga region header object corresponding to the data object. Whilefacilitating storage of the data object, the method continues at step468 where the processing module outputs another set of transient slicesof another revision to the DSN memory at time intervals less than aminimum interval period. The outputting includes identifying the timeinterval, upon the time interval generating other data slice accessrequests that includes the write transient slice requests, the set ofslice names, the set of transient slices, and a new revision (e.g.,increment a previous revision by one).

When storage in the data object has been completed, the method continuesat step 470 where the processing module deletes the transient slices.The processing module may detect completion of storage of the dataobject when receiving favorable write slice responses corresponding towrite slice requests sent to the DSN memory to facilitate storage of thedata object. The deleting includes generating a set of delete data sliceaccess requests that includes the set of slice names and outputting theset of delete slice access requests to the DSN memory. The methodcontinues with step 424 of FIG. 44 where the processing module indicatesthat the write sequence is not active.

FIGS. 47A, 47G, and 47H are schematic block diagrams of an embodiment ofa dispersed storage network (DSN) illustrating example steps of storingdata 488. The DSN includes the distribute storage and task (DST) clientmodule 34 and the network 24 of FIG. 1, and any number of DST executionunits. The DST execution units provides DSN memory. The DST executionunits may be implemented utilizing the DST execution units 36 (e.g.,storage units) of FIG. 1. In a specific example, the DST execution unitsmay include a set of DST execution units 1-5. As another specificexample, the DST execution units includes an alternative set of DSTexecution units with less than 5 DST execution units (e.g., 4 DSTexecution units). As yet another specific example, the DST executionunits may include another alternative set of DST execution units withmore than five DST execution units (e.g., 6 DST execution units). TheDST client module 34 includes the outbound dispersed storage (DS)processing module 80 of FIG. 3. The outbound DST processing module 80includes the DS error encoding 112 of FIG. 4 and a DSN address generator480, a slice selector 482, a slice name selector 484, and a requestgenerator 486.

The DSN functions to store the data 488 in the DST execution units asencoded data slices. The data 488 includes one of a data object, a datafile, a plurality of data objects, a plurality of data files, a datasegment of the data object (e.g., where the data 488 is divided into aplurality of data segments utilizing a segmentation scheme), a datasegment of the data file, a group of data segments of the data object,and a group of data segments of the data file. The data object includesat least one of text, an image, audio, video, multimedia, etc. The datafile includes the data object organized into a storage/communicationstructure in accordance with a standard associated with one or more of astorage system and a communication system.

FIG. 47A illustrates initial steps of the example steps of the storingof the data 488 in at least some of the set of DST execution units 1-5.As a specific example, the outbound DS processing module 80 receives thedata 488 for storage in the DSN memory. The outbound DS processingmodule 80 ascertains dispersed storage error encoding parameters 489(e.g., determines, accesses pre-established parameters based on a datatype, utilizes fixed parameters for a requesting entity, establishesprogrammable parameters based on a storage reliability requirement,performs a table look up, utilizes a user selection of parameters, etc.)for encoding the data 488. The DS error encoding parameters 489 includesone or more of a width number “n”, a decode threshold number, andencoding algorithm identifier, and an encoding matrix. The width number“n” indicates a number of encoded data slices that are generated whenencoding a data segment of the data 488. The decode threshold numberindicates a minimum number of encoded data slices for the set of encodeddata slices that are required to recover the data segment. For instance,the outbound DS processing module 80 performs a system registry lookupbased on the requesting entity to ascertain the dispersed storage errorencoding parameters 489 to include a width of n=5 and a decode thresholdof 3.

Having ascertained the dispersed storage error encoding parameters 489,outbound DS processing module 80 ascertains “p” number of DST executionunits of the DSN memory for the storing of an encoded version of thedata 488. As a specific example, the outbound DS processing module 80performs a table lookup to ascertain the dispersed storage errorencoding parameters and the “p” number of DST execution units such that“n” equals “p”. For instance, n=p=5. As another specific example, theoutbound DS processing module 80 performs another table lookup toascertain the dispersed storage error encoding parameters and the “p”number of DST execution units such that “n” is less than “p”. Forinstance, n=5 and p=6. As another instance, n=4 and p=5.

Having ascertained the “p” number of DST execution units, the outboundDS processing module 80 ascertains a storage mapping that maps encodeddata slices to DST execution units for storing the encoded version ofthe data 488. As a specific example, the outbound DS processing moduleaccesses a slice selection table and a storage unit selection table toidentify which encoded data slices of each set of “n” encoded dataslices to store in which of the “p” DST execution units, where theencoded version of the data includes sets of “n” encoded data slices. Asa more specific example, the DSN address generator 480 generates a setof slice names 1-n based on the data 488. Each slice name includes apillar number to identify a slice from n slices per set, a vaultidentifier (ID) based on the requesting entity, a vault generation ID,an object ID that is unique for the data 488, and a segment ID of one ormore segment IDs.

Having generated the set of slice names 1-n, the slice selector 482accesses the slice selection table to identify targeted encoded dataslices T1 through TT of each set of “n” encoded data slices. Forexample, the slice selector 482 identifies a DSN address range of theset of slice names 1-n and accesses the slice selection table toidentify the targeted encoded data slices T1 through TT. For instance,the slice selector 482 identifies a seventh DSN address range, accessesthe slice selection table, and identifies encoded data slices 2, 3, and4 as targeted encoded data slices T1-T3 when three encoded data slicesare to be selected. The selecting of the targeted encoded data slices isdiscussed in greater detail with reference to FIGS. 47B-C.

Having identified the targeted encoded data slices T1 through TT, theslice name selector 484 selects slice names 1-T, of the set of slicenames 1-n, associated with the targeted encoded data slices T1 throughTT. The request generator 486 identifies the DST execution units forstoring the encoded version of the data 488 by accessing the storageunit selection table based on the identified DSN address range of theset of slice names 1-n. For instance, the request generator 486 accessesthe storage unit selection table based on the seventh DSN address rangeand identifies DST execution units 2, 3, and 4 as the DST executionunits for storing the encoded version of the data 488. The selecting ofthe DST execution units is discussed in greater detail with reference toFIGS. 47 D-F.

The DS error encoding 112 encodes the data 488 in accordance with thedispersed storage error encoding parameters 489 to produce sets ofencoded data slices. The sets of encoded data slices includes “n” numberof encoded data slices. The request generator 486 generates writerequests for storing, in accordance with the storage mapping, encodeddata slices of the sets of encoded data slices in a pattern (e.g., thetargeted encoded data slices T1-TT selected by the across the sliceselector 482) across the “p” number of DST execution units (e.g., theidentified DST execution units), where less than the “p” number of DSTexecution units stores an encoded data slice of the set of encoded dataslices or a subset of the set of encoded data slices. As a specificexample, the request generator 486 generates write slice requests 490that includes the encoded data slices 2, 3, and 4 and slice names 1-Tcorresponding to the encoded data slices 2, 3, and 4. Next, the requestgenerator 486 outputs, via the network 24, the write slice requests 480as write slice requests T1, T2, and T3 to DST execution units 2, 3, and4. DST execution unit 2 stores encoded data slice 2 in memory of the DSTexecution unit 2 etc.

FIGS. 47B-C are diagrams illustrating examples of slice selection tables492 that include a dispersed storage network (DSN) address range field494, a target set field 486, and fields for target slices (e.g., targetslices 1-3 when a storage mapping maps three encoded data slices tothree DST execution units for storing an encoded version of data wherethe data is encoded using a dispersed storage error coding function toproduce sets of encoded data slices). The slice selection table 492includes a target set number of entries. For example, the sliceselection table 492 includes five entries when the target set number isfive. Each entry of the slice selection table 482 includes a DSN addressrange entry of the DSN address range field 494, a target set entry ofthe target set field 496, and target slice entries for the target slicefields. The target set entry of the target set field 496 identifies acombination of encoded data slices of a set of encoded data slices. Suchcombinations may be identified to accommodate a rotation pattern. Forexample, a DSN address range associated with the data is determined inaccordance with a address range scheme (e.g., dividing a DSN address bya number of DSN address ranges to generate a DSN address rangeidentifier), the target set number of entries is determined (e.g., alookup), and the target set entry is calculated in accordance with aformula: target set=DSN address range modulo (target set number ofentries). For instance, target set 2=7 modulo 5; when the DSN addressrange is a seventh address range and the target set number of entries is5.

In particular, FIG. 47B illustrates an example of the slice selectiontable 492 when the combinations of encoded data slices are to utilizeeach encoded data slice an equal number of times across the five targetsets. Such a rotation pattern enables a uniform rotation pattern. Forinstance, each encoded data slice is selected three times of the fivetarget sets when each target set of targeted encoded data slices isutilized. The storage mapping is ascertained such that, from set to setof encoded data slices, a fixed sub-set of an “n” number of encoded dataslices of the sets of encoded data slices are stored in at least one ofa fixed sub-set of a “p” number of DST execution units and a varyingsub-set of the “p” number of DST execution units (e.g., as subsequentlyselected by utilizing a storage unit selection table). For instance, thefixed sub-set of encoded data slices (e.g., encoded data slices 2, 3,and 4) is selected for storage for each set of encoded data slices whena common DSN address range entry is associated with each set of encodeddata slices (e.g., a source name DSN address that corresponds to thedata). As another instance, another fixed sub-set of encoded data slices(e.g., encoded data slices 3, 4, and 5) is selected for storage of eachset of other encoded data slices of other data based on another sourcename DSN address.

Alternatively, the storage mapping may be ascertained such that, fromset to set of encoded data slices, a varying sub-set of the “n” numberof encoded data slices are stored in the at least one of the fixedsub-set of the “p” number of DST execution units and the varying sub-setof the “p” number of DST execution units (e.g., as subsequently selectedby utilizing the storage unit selection table). For instance, thevarying sub-set of encoded data slices is selected for storage for eachset of encoded data slices when DSN address range entries that eachinclude a segment number are utilized.

Alternatively, FIG. 47C illustrates another example of the sliceselection table 492 when the combination of encoded data slices is toutilize a fixed common subset of encoded data slices across the fivetarget sets when the rotation pattern is a fixed rotation pattern. Forinstance, targeted encoded data slices 1-3 are utilized for each DSNaddress range. The storage mapping is ascertained such that, from set toset of encoded data slices, a fixed sub-set of the “n” number of encodeddata slices of the sets of encoded data slices are stored in the atleast one of the fixed sub-set of the “p” number of DST execution unitsand the varying sub-set of the “p” number of DST execution units (e.g.,as subsequently selected by utilizing a storage unit selection table).

FIGS. 47D-F are diagrams illustrating examples of a storage unitselection table that includes the DSN address range field 494 and thetarget set field 496 of FIG. 47B, and target slice fields that maptarget encoded data slices to storage units (e.g., DST execution units).Dispersed storage error encoding parameters (e.g., including width “n”)are ascertained and a “p” number of storage units are ascertained suchthat “n” equals “p”. For instance, 5 DST execution units are selectedfor storing encoded data slices where data divided into data segmentsand each data segment is dispersed storage error encoded to produce setsof encoded data slices where each set of encoded data slices includes“n” encoded data slices.

The storage mapping may be ascertained such that either a fixed sub-setor a varying sub-set of the “n” number of encoded data slices of each ofthe sets of encoded data slices are stored in at least one of a fixedsub-set of the “p” number of storage units and a varying sub-set of the“p” number of storage units. As a specific example, the targeted encodeddata slices are stored in the fixed sub-subset of the “p” number ofstorage units when a DSN address range entry of the DSN address rangefield 494 includes a DSN address associated with the data (e.g., asource name of the data). For instance, storage units 2, 3, and 4 areselected for storage of target slices 1-3 when the source name of thedata is associated with a seventh DSN address range and a second targetset. As another specific example, the targeted encoded data slices arestored in the varying sub-subset of the “p” number of storage units whenthe DSN address range entry of the DSN address range field 494 includesa DSN address associated with each data segment of the data segments ofthe data (e.g., a segment number). For instance, storage units 3, 4, and5 are selected for storage of target slices 1-3 when the segment numberof a first data segment is associated with an eighth DSN address rangeand a third target set. As another instance, storage units 4, 5, and 1are selected for storage of target slices 1-3 when the segment number ofa second data segment is associated with a ninth DSN address range and afourth target set.

FIG. 47E illustrates further combinations of the storage units of FIG.47D, where more permutations of the “p” number (e.g., 5) of storageunits are provided for storage unit selection based on DSN addressranges. As a specific example, storage units 2, 3, and 5 are selectedfor storage of target slices 1-3 when the source name of the data isassociated with a seventh DSN address range (e.g., a source name) and asecond target set. As another specific example, storage units 3, 4, and1 are selected for storage of target slices 1-3 when the segment numberof a first data segment is associated with an eighth DSN address rangeand a third target set. As another instance, storage units 4, 5, and 2are selected for storage of target slices 1-3 when the segment number ofa second data segment is associated with a ninth DSN address range and afourth target set.

FIG. 47F illustrates yet another example of the storage unit selectiontable 494 where the dispersed storage error encoding parameters areascertained and the “p” number of storage units are ascertained suchthat “n” is less than “p”. For instance, six DST execution units areselected as the “p” number and five is selected for the width “n”. Thestorage mapping is ascertained such that, from set to set of encodeddata slices, the targeted encoded data slices of the “n” number ofencoded data slices of the plurality of sets of encoded data slices arestored in at least one of a fixed sub-set and a varying sub-set of the“p” number of storage units. As a specific example, the targeted encodeddata slices are stored in the fixed sub-subset of the “p” number ofstorage units when a DSN address range entry of the DSN address rangefield 494 includes a DSN address associated with the data (e.g., asource name of the data). For instance, storage units 2, 3, and 7 areselected for storage of target slices 1-3 when the source name of thedata is associated with a 27th DSN address range and a seventh targetset. As another specific example, the targeted encoded data slices arestored in the varying sub-subset of the “p” number of storage units whenthe DSN address range entry of the DSN address range field 494 includesa DSN address associated with each data segment of the data segments ofthe data (e.g., a segment number). For instance, storage units 5, 1, and3 are selected for storage of target slices 1-3 when the segment numberof a first data segment is associated with a 30th DSN address range anda 10th target set. As another instance, storage units 6, 1, and 2 areselected for storage of the target slices 1-3 when the segment number ofa second data segment is associated with a 31st DSN address range and aeleventh target set.

FIG. 47G illustrates further steps of the example steps of the storingof the data 488 in at least some of the set of DST execution units 1-5.DST execution units receiving the write slice request of FIG. 1 store anencoded data slice of the corresponding request and issues, via thenetwork 24, a write slice response of write slice responses T1-T3 to theDST client module 34 as write slice responses 500. Each write sliceresponse includes an indicator with regards to success of storing anassociated encoded data slice. For instance, DST execution unit 2generates write slice response T1 indicating that the encoded data slice2 was successfully stored.

In another instance, DST execution unit 3 generates write slice responseT2 indicating that the encoded data slice 3 was not successfully stored.

The request generator 486 receives the write slice responses 500. Therequest generator 486 determines whether a target number of favorablewrite slice responses 500 has been received within a timeframe ofsending the write slice requests 490. For example, the request generator486 determines that the target number of favorable write slice responses500 has not been received within a time frame when receiving write sliceresponse T2 indicating that the encoded data slice 3 was notsuccessfully stored. When the request generator 486 determines that thetarget number of favorable write slice responses 500 has not beenreceived, for each unsuccessfully stored encoded data slice, the requestgenerator 486 obtains (e.g., receives from the slice selector 482)another encoded data slice of the set of “n” encoded data slices. Forinstance, slice selector 482 selects encoded data slice 5 from the setof encoded data slices when encoded data slice 5 has not been previouslyoutput for storage in the DSN memory.

FIG. 47H illustrates further steps of the example steps of the storingof the data 488 and at least some of the set of DST execution units 1-5.The request generator 486, for each unsuccessfully stored encoded dataslice, issues, via the network 24, a write slice request 490 to acorresponding DST execution unit, where the write slice request 490includes the associated obtained encoded data slice and an associatedslice name from the slice name selector 484. For instance, the requestgenerator 486 generates the write slice request 490 to include theencoded data slice 5 and a slice name 5 associated with the encoded dataslice 5. Having generated the write slice request 490, the requestgenerator 486 sends, via the network 24, the write slice request 490 toDST execution unit 5. Alternatively, or in addition to, the outbound DSTprocessing module 80 associates a source name of the data 488 withidentifiers of the DST execution units utilized for storage of each setof encoded data slices.

Alternatively, or in addition to, the encoded data slice 5 istemporarily stored in the DST execution unit 5 until a rebuildingprocess scans the set of DST execution units to identify a slice errorassociated with encoded data slice 3 and facilitates rebuilding ofencoded data slice 3 for storage in the DST execution unit 3. When theencoded data slice 3 has been successfully stored, the rebuildingprocess deletes temporarily stored encoded data slice 5 from DSTexecution unit 5 in accordance with the storage mapping.

When reading the data 488 from the DSN memory, the DST client module 34obtains the source name associated with data 488, identifies, for eachdata segment, the target storage set, issues read slice requests to thetarget DST execution units of the target storage set, receives at leasta decode threshold number of encoded data slices from the target DSTexecution units, and decodes the at least the decode threshold number ofencoded data slices using the dispersed storage error coding function toreproduce the data 488. When the DST client module 34 does not receivethe at least the decode threshold number of encoded data slices from thetarget DST execution units, the DST client module 34 issues one or moreadditional read slice requests to other DST execution units of the DSNmemory to retrieve further encoded data slices. Next, the DST clientmodule 34 decodes at least some of the received encoded data slices fromthe target DST execution units and at least one encoded data slice fromthe other DST execution units to reproduce the data 488.

FIG. 47I is a flowchart illustrating an example of storing data. Themethod begins at step 502 where a processing module (e.g., of adispersed storage processing module of a dispersed storage network(DSN)) receives data for storage in DSN memory. The data includes one ofa data object, a data file, a plurality of data objects, a plurality ofdata files, a data segment of the data object (e.g., where the data isdivided into a plurality of data segments utilizing a segmentationscheme), a data segment of the data file, a group of data segments ofthe data object, and a group of data segments of the data file.

The method continues at step 504 where the processing module ascertains(e.g., determines, utilizes pre-established parameters, accesses fixedparameters, reads programmable parameters, performs a table look up,utilizes user selected parameters, etc.) dispersed storage errorencoding parameters for encoding the data. The method continues at step506 where the processing module ascertains “p” number of storage unitsof the DSN memory for the storing an encoded version of the data. As aspecific example, the processing module performs a system registry tablelookup to ascertain the dispersed storage error encoding parameters andthe “p” number of storage units such that a dispersed storage errorencoding parameter “n” equals “p”. The dispersed storage error encodingparameter “n” specifies a number of encoded data slices for a set ofencoded data slices where the data is dispersed storage error encodingutilizing the dispersed storage error encoding parameters to producesets of encoded data slices that includes the set of encoded dataslices. As another specific example, the processing module ascertainsthe dispersed storage error encoding parameters and the “p” number ofstorage units such that “n” is less than “p”.

The method continues at step 508 where the processing module ascertainsa storage mapping that maps encoded data slices (e.g., of the sets ofencoded data slices) to the storage units for storing the encodedversion of the data. As a specific example, the processing moduleperforms a table lookup to ascertain the storage mapping such that, fromset to set, a fixed sub-set of the “n” number of encoded data slices ofthe sets of encoded data slices are stored in a fixed sub-set of the “p”number of storage units. For instance, similar encoded data sliceselections are stored in a common set of storage units for all the setsof encoded data slices. As another specific example, the processingmodule performs the table lookup to ascertain the storage mapping suchthat, from set to set, a varying sub-set of the “n” number of encodeddata slices of the sets of encoded data slices are stored in the fixedsub-set of the “p” number of storage units. For instance, differentencoded data slice selections for each set are stored in the common setof storage units. As yet another specific example, the processing moduleperforms the table lookup to ascertain the storage mapping such that,from set to set, the fixed sub-set of the “n” number of encoded dataslices of the plurality of sets of encoded data slices are stored in avarying sub-set of the “p” number of storage units. For instance, thesimilar encoded data slice selections are stored in different sets ofstorage units. As a further specific example, the processing moduleperforms the table lookup to ascertain the storage mapping such that,from set to set, the varying sub-set of the “n” number of encoded dataslices of the plurality of sets of encoded data slices are stored in thevarying sub-set of the “p” number of storage units. For instance, thedifferent encoded data slice selections for each set are stored in thedifferent sets of storage units.

Alternatively, in another example, when the processing module ascertainsthe dispersed storage error encoding parameters and the “p” number ofstorage units such that “n” is less than “p”, the processing moduleascertains the storage mapping such that, from set to set, the “n”number of encoded data slices of the sets of encoded data slices arestored in the fixed sub-set of the “p” number of storage units. Asanother alternative in another example, when the processing moduleascertains the dispersed storage error encoding parameters and the “p”number of storage units such that “n” is less than “p”, the processingmodule ascertains the storage mapping such that, from set to set, the“n” number of encoded data slices of the sets of encoded data slices arestored in the varying sub-set of the “p” number of storage units.

The method continues at step 510 where the processing module encodes thedata in accordance with the dispersed storage error encoding parametersto produce the sets of encoded data slices. Each set of encoded dataslices includes “n” number of encoded data slices. The method continuesat step 512 where the processing module generates one or more sets ofwrite requests for storing, in accordance with the storage mapping,encoded data slices of the sets of encoded data slices in a patternacross the “p” number of storage units, where less than the “p” numberof storage units stores an encoded data slice of the set of encoded dataslices or a subset of the set of encoded data slices.

FIG. 48 is a flowchart illustrating another example of storing data thatincludes similar steps to FIG. 42. The method begins with step 390 ofFIG. 42 where a processing module (e.g., of a dispersed storage (DS)processing module) receives a write data object request that includes adata object for storage and a data name. The method continues at step514 where the processing module determines a source name for the databased on the data name. For example, the processing module performs avault identifier (ID) look up to produce a vault ID based on arequesting entity ID, generates an object number based on a randomnumber, and generates the source name to include a default generationID, the vault ID, and the object number. The method continues at step516 where the processing module identifies a source name rangecorresponding to the source name. For example, the processing moduleperforms a lookup in a source name range list utilizing the source name.

The method continues at step 518 where the processing module identifiesa storage pattern corresponding to the source name from a predeterminedlist of available storage patterns associated with the identified sourcename range. The identifying includes identifying a source name offset ofthe source name within the source name range (e.g., calculating adifference between a starting address of the source name range and thesource name), identifying a number of storage patterns of thepredetermined list of available storage patterns (e.g., a lookup), andtaking the source name offset modulo of the number of storage patternsto produce a storage pattern identifier of the identified storagepattern. For example, with an affinity target number of 3, and pillarwidth of 5, there are 5 storage patterns including (e.g., by pillarnumber): (1, 2, 3), (2, 3, 4), (3, 4, 5), (4, 5, 1), (5, 1, 2). Everypillar is used exactly 3 times. When a source name falls into the Nthsub range (e.g., source name offset) of the source name range, forexample the 5,672nd, the sub-range number (e.g., source name offset) istaken modulo the number of groups (5), which yields 5,672 modulo 5=2.Therefore affinity group two is selected (3, 4, 5). Thus the slices forDS units with pillar number 3, 4, and 5 will be written, but the slicesfor pillars 1 and 2 are written only as a last resort (if one of thestores 3, 4, or 5 is down) as is discussed below.

The method continues at step 520 where the processing module identifiesa set of DS units that corresponds to the source name. For example, theprocessing module accesses a source name to physical location table toidentify the set of DS units. The method continues at step 522 where theprocessing module identifies affinity DS units, of the set of DS units,that corresponds to the storage pattern. For example, the processingmodule utilizes the storage pattern identifier to access a list ofstorage patterns corresponding to the source name range to identify theaffinity DS units.

The method continues at step 524 where the processing module partitionsthe data object to produce a plurality of data segments in accordancewith a segmentation scheme. For each data segment of the plurality ofdata segments, the method continues at step 526 where the processingmodule encodes the data segment to produce encoded affinity slices.Alternatively, the processing module produces a full pillar width numberof slices. The method continues at step 528 where the processing moduleoutputs the encoded affinity slices to the identified affinity DS units.The outputting includes generating affinity slice names corresponding toeach of the encoded affinity slices, generating write slice requeststhat includes the encoded affinity slices and the affinity slice names,and outputting the write slice requests to the identified affinity DSunits.

When not receiving confirmation of storage of each of the encodedaffinity slices within a timeframe, the method continues at step 530where the processing module outputs one or more other encoded dataslices of the data segment to one or more other DS units of a set of DSunits. The processing module may detect confirmation of storage byreceiving a write slice response that includes a status code withregards to favorability of storing and associated slice. The outputtingincludes selecting a number of other slices to encode the data segmentto produce the other encoded data slices to provide in total a writethreshold number of favorable confirmed stored slices based on a numberof unfavorable responses or no responses within a response time frame.Alternatively, the processing module outputs encoded affinity slices toone or more of the other DS units as foster slices until the affinity DSunits are back online and can receive and successfully stored encodedaffinity slices.

FIG. 49 is a flowchart illustrating another example of storing data,which includes similar steps to FIGS. 42 and 48. The method begins withstep 390 of FIG. 42 where a processing module (e.g., a dispersed storage(DS) processing module) receives a write data object request thatincludes a data object for storage and a data name. The method continueswith steps 514-526 of FIG. 48 where the processing module determines asource name for the data based on the data name, identifies a sourcename range corresponding to the source name, identifies a storagepattern corresponding to the source name from a predetermined list ofavailable storage patterns associated with the identified source namerange, identifies a set of DS units that corresponds to the source name,identifies affinity DS units, of the set of DS units, that correspond tothe storage pattern, partitions the data object to produce a pluralityof data segments, and for each data segment, encodes the data segment toproduce encoded affinity slices.

The method continues at step 532 where the processing module encodes thedata segment to produce one or more other encoded data slices. Theencoding may be based on one or more of a predetermination, a previouserror rate, and an expected number of failures. For example, theprocessing module determines a number of the one or more other encodeddata slices to be two when an expected number of failures is one withregards to storing encoded affinity slices. The method continues at step534 where the processing module issues write slice requests, thatincludes the encoded affinity slices, to the affinity DS units. Theissuing includes generating write slice requests to include the encodedaffinity slices and outputting the write slice requests to the affinityDS units. The method continues at step 536 where the processing moduleissues write slice requests, that includes the other encoded dataslices, to other DS units of the set of DS units. The issuing includesgenerating write slice requests to include the other encoded slices andoutputting the write slice requests to the other DS units.

The method continues at step 538 where the processing module determineswhether a favorable number of write slice responses has been receivedfrom the affinity DS units. The determining may be based on comparingthe number of favorable responses to a favorability threshold (e.g., atarget affinity number). The method branches to step 542 when thefavorable number of write slice responses has not been received. Themethod continues to step 540 when the favorable number of write sliceresponses has been received. The method continues at step 540 where theprocessing module issues rollback requests, with regards to the otherencoded data slices, to the other DS units of the set of DS units. Theissuing includes generating the rollback requests to include atransaction number of corresponding write requests and outputting therollback request to the other DS units. When the favorable number ofwrite slice responses has not been received, the method continues atstep 542 where the processing issues commit requests, with regards tothe other encoded data slices, to the other DS units of the set of DSunits. The issuing includes generating the commit requests to includethe transaction number of the corresponding write requests andoutputting the commit requests to the other DS units.

FIG. 50 is a flowchart illustrating another example of storing data. Themethod begins with step 544 where a processing module (e.g., of adispersed storage (DS) processing module) identifies a DS unit subset ofa set of DS units for storage of a subset of encoded data slices of aset of encoded data slices. The identifying may be based on one or moreof a storage pattern, a dispersed storage error coding parameters, adispersed storage error coding function, a predetermination, a request,a failure message, a DS unit availability indicator, and a reliabilityrequirement. For example, the processing module identifies a DS unitsubset that includes 12 DS units when the set of DS units includes 16 DSunits, a decode threshold is 10, and a write threshold is 12.

The method continues at step 546 where the processing module generates amodified encoding matrix from an encoding matrix based on the identifiedDS units subset. For example, the processing module matches rows of theencoding matrix that correspond to slices that map to the DS unitsubset. For example, the processing module includes rows 1-10, 14, and15 when the DS unit subset includes DS units 1-10, 14, and 15. Themethod continues at step 548 where the processing module encodes a datasegment utilizing the modified encoding matrix to produce the subset ofencoded data slices. For example, the processing module matrixmultiplies the data segment by the modified encoding matrix to producethe subset of encoded data slices. The method continues at step 550where the processing module outputs the subset of encoded data slices tothe DS unit subset for storage therein. The outputting includesgenerating a sub-set of slice names, generating a sub-set of write slicerequests that includes the sub-set of slice names and the sub-set ofencoded data slices, and outputting the sub-set of write slice requeststo the DS unit subset.

FIG. 51A is a diagram illustrating an example of a virtual dispersedstorage network (DSN) address to physical location table 552 thatincludes an address range field 554, a site identifier (ID) field 556, acabinet ID field 558, a rack position field 560, and a dispersed storage(DS) unit ID field 562. The virtual DSN address to physical locationtable 552 includes a plurality of entries. Each entry of the pluralityof entries includes a DSN address range entry corresponding to theaddress range field 554, a site ID entry for a site of one or more sitesof a DSN corresponding to the site ID field 556, a cabinet ID entrycorresponding to a cabinet installed at a site of the cabinet ID field558, a rack position entry corresponding to an installation position ofa DS unit within the cabinet of the rack position field 560, and a DSunit ID of the DS unit in the DS unit ID field 562. For example, a DSunit that is associated with a DS unit ID of 41F is installed in asecond rack position of a first cabinet at a first site where the DSunit is assigned to a DSN address range of 1000-1999.

The virtual DSN address to physical location table 552 may be sorted inaccordance with a lexicographical order (e.g., as illustrated) such thatDS units with adjacent DSN address ranges are many times installed inpseudo-adjacent physical locations. Such an implementation approachfacilitates a system reliability improvement when stored encoded dataslices are moved from a source DS unit to a destination DS unit that isphysically adjacent and logically adjacent. The method of assignment ofthe DS units to address ranges using the lexicographical ordering isdiscussed in more detail with reference to FIG. 51B.

FIG. 51B is a flowchart illustrating an example of assigning dispersedstorage network (DSN) address ranges. The method begins with step 564where a processing module (e.g., of a managing module) identifies aplurality of dispersed storage (DS) units. The identifying may be basedon one or more of retrieving DS unit identifiers from a system registry,accessing a factory build list, receiving a message, initiating a queryof available new DS units, and receiving responses from one or more DSunits. For each DS unit of the plurality of DS units, the methodcontinues at step 566 where the processing module generates aconcatenated temporary identifier that includes one or more associatedattributes. An attribute of the one or more associated attributesincludes one or more of a physical location, a set of global positioningsatellite coordinates, a street address, a storage container number, avehicle number, a container location identifier, a building number, afloor number, a site identifier (ID), a cabinet ID, a rack position, anda sub-rack position.

The method continues at step 568 where the processing module sorts alist of the concatenated temporary identifiers based on lexicographicalorder. For example, DS units associated with a common site are groupedtogether and sorted by cabinets and then sorted by rack positions withineach cabinet (e.g., as illustrated in FIG. 51A). The method continues atstep 570 where the processing module identifies a plurality of orderedDSN address ranges for assignment to the plurality of DS units. Theidentifying may be based on one or more of a predetermined registryvalue, a management input, and a number of higher order attributes ofthe sorted list. For example, the number of ordered DSN address rangesmay be identified as a number of rack positions per cabinet multiplyingby the number of total cabinets across all sites and the ordered DSNaddress ranges are identified as a total DSN address range space dividedby the number of ordered DSN address ranges. The method continues atstep 572 where the processing module associates the plurality of orderedDSN address ranges with DS units in order of the sorted list. Theassociating includes mapping one-for-one DS units to DSN address rangesand updating a virtual DSN address to physical location table.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “operably coupled to”, “coupled to”, and/or “coupling” includesdirect coupling between items and/or indirect coupling between items viaan intervening item (e.g., an item includes, but is not limited to, acomponent, an element, a circuit, and/or a module) where, for indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.As may even further be used herein, the term “operable to” or “operablycoupled to” indicates that an item includes one or more of powerconnections, input(s), output(s), etc., to perform, when activated, oneor more its corresponding functions and may further include inferredcoupling to one or more other items. As may still further be usedherein, the term “associated with”, includes direct and/or indirectcoupling of separate items and/or one item being embedded within anotheritem. As may be used herein, the term “compares favorably”, indicatesthat a comparison between two or more items, signals, etc., provides adesired relationship. For example, when the desired relationship is thatsignal 1 has a greater magnitude than signal 2, a favorable comparisonmay be achieved when the magnitude of signal 1 is greater than that ofsignal 2 or when the magnitude of signal 2 is less than that of signal1.

As may also be used herein, the terms “processing module”, “processingcircuit”, and/or “processing unit” may be a single processing device ora plurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing module, module, processingcircuit, and/or processing unit may be, or further include, memoryand/or an integrated memory element, which may be a single memorydevice, a plurality of memory devices, and/or embedded circuitry ofanother processing module, module, processing circuit, and/or processingunit. Such a memory device may be a read-only memory, random accessmemory, volatile memory, non-volatile memory, static memory, dynamicmemory, flash memory, cache memory, and/or any device that storesdigital information. Note that if the processing module, module,processing circuit, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

The present invention has been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claimed invention. Further, theboundaries of these functional building blocks have been arbitrarilydefined for convenience of description. Alternate boundaries could bedefined as long as the certain significant functions are appropriatelyperformed. Similarly, flow diagram blocks may also have been arbitrarilydefined herein to illustrate certain significant functionality. To theextent used, the flow diagram block boundaries and sequence could havebeen defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claimed invention. One of average skill in the artwill also recognize that the functional building blocks, and otherillustrative blocks, modules and components herein, can be implementedas illustrated or by discrete components, application specificintegrated circuits, processors executing appropriate software and thelike or any combination thereof.

The present invention may have also been described, at least in part, interms of one or more embodiments. An embodiment of the present inventionis used herein to illustrate the present invention, an aspect thereof, afeature thereof, a concept thereof, and/or an example thereof. Aphysical embodiment of an apparatus, an article of manufacture, amachine, and/or of a process that embodies the present invention mayinclude one or more of the aspects, features, concepts, examples, etc.described with reference to one or more of the embodiments discussedherein. Further, from figure to figure, the embodiments may incorporatethe same or similarly named functions, steps, modules, etc. that may usethe same or different reference numbers and, as such, the functions,steps, modules, etc. may be the same or similar functions, steps,modules, etc. or different ones.

While the transistors in the above described figure(s) is/are shown asfield effect transistors (FETs), as one of ordinary skill in the artwill appreciate, the transistors may be implemented using any type oftransistor structure including, but not limited to, bipolar, metal oxidesemiconductor field effect transistors (MOSFET), N-well transistors,P-well transistors, enhancement mode, depletion mode, and zero voltagethreshold (VT) transistors.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of the various embodimentsof the present invention. A module includes a processing module, afunctional block, hardware, and/or software stored on memory forperforming one or more functions as may be described herein. Note that,if the module is implemented via hardware, the hardware may operateindependently and/or in conjunction software and/or firmware. As usedherein, a module may contain one or more sub-modules, each of which maybe one or more modules.

While particular combinations of various functions and features of thepresent invention have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent invention is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by one or more processing modules of one or more computing devices of a dispersed storage network (DSN), the method comprises: receiving data for storage in DSN memory; ascertaining dispersed storage error encoding parameters for encoding the data; ascertaining “p” number of storage units of the DSN memory for the storing an encoded version of the data; ascertaining a storage mapping that maps encoded data slices to storage units for storing the encoded version of the data; encoding the data in accordance with the dispersed storage error encoding parameters to produce a plurality of sets of encoded data slices, wherein a set of the plurality of sets of encoded data slices includes “n” number of encoded data slices; and generating a plurality of write requests for storing, in accordance with the storage mapping, encoded data slices of the plurality of sets of encoded data slices in a pattern across the “p” number of storage units, wherein less than the “p” number of storage units stores an encoded data slice of the set of the plurality of sets of encoded data slices or a subset thereof.
 2. The method of claim 1 further comprises: ascertaining the dispersed storage error encoding parameters and the “p” number of storage units such that “n” equals “p”; and ascertaining the storage mapping such that, from set to set, a fixed sub-set of the “n” number of encoded data slices of the plurality of sets of encoded data slices are stored in a fixed sub-set of the “p” number of storage units.
 3. The method of claim 1 further comprises: ascertaining the dispersed storage error encoding parameters and the “p” number of storage units such that “n” equals “p”; and ascertaining the storage mapping such that, from set to set, a varying sub-set of the “n” number of encoded data slices of the plurality of sets of encoded data slices are stored in a fixed sub-set of the “p” number of storage units.
 4. The method of claim 1 further comprises: ascertaining the dispersed storage error encoding parameters and the “p” number of storage units such that “n” equals “p”; and ascertaining the storage mapping such that, from set to set, a fixed sub-set of the “n” number of encoded data slices of the plurality of sets of encoded data slices are stored in a varying sub-set of the “p” number of storage units.
 5. The method of claim 1 further comprises: ascertaining the dispersed storage error encoding parameters and the “p” number of storage units such that “n” equals “p”; and ascertaining the storage mapping such that, from set to set, a varying sub-set of the “n” number of encoded data slices of the plurality of sets of encoded data slices are stored in a varying sub-set of the “p” number of storage units.
 6. The method of claim 1 further comprises: ascertaining the dispersed storage error encoding parameters and the “p” number of storage units such that “n” is less than “p”; and ascertaining the storage mapping such that, from set to set, the “n” number of encoded data slices of the plurality of sets of encoded data slices are stored in a fixed sub-set of the “p” number of storage units.
 7. The method of claim 1 further comprises: ascertaining the dispersed storage error encoding parameters and the “p” number of storage units such that “n” is less than “p”; and ascertaining the storage mapping such that, from set to set, the “n” number of encoded data slices of the plurality of sets of encoded data slices are stored in a varying sub-set of the “p” number of storage units.
 8. The method of claim 1, wherein the data comprises one of: a data object; a data file; a plurality of data objects; a plurality of data files; a data segment of the data object; a data segment of the data file; a group of data segments of the data object; and a group of data segments of the data file.
 9. A dispersed storage (DS) module of a dispersed storage network (DSN), the DS module comprises: a first module, when operable within a computing device, causes the computing device to: receive data for storage in DSN memory; ascertain dispersed storage error encoding parameters for encoding the data; ascertain “p” number of storage units of the DSN memory for the storing an encoded version of the data; and ascertain a storage mapping that maps encoded data slices to storage units for storing the encoded version of the data; a second module, when operable within the computing device, causes the computing device to: encode the data in accordance with the dispersed storage error encoding parameters to produce a plurality of sets of encoded data slices, wherein a set of the plurality of sets of encoded data slices includes “n” number of encoded data slices; and a third module, when operable within the computing device, causes the computing device to: generate a plurality of write requests for storing, in accordance with the storage mapping, encoded data slices of the plurality of sets of encoded data slices in a pattern across the “p” number of storage units, wherein less than the “p” number of storage units stores an encoded data slice of the set of the plurality of sets of encoded data slices or a subset thereof.
 10. The DS module of claim 9 further comprises: the first module, when operable within the computing device, further causes the computing device to: ascertain the dispersed storage error encoding parameters and the “p” number of storage units such that “n” equals “p”; and ascertain the storage mapping such that, from set to set, a fixed sub-set of the “n” number of encoded data slices of the plurality of sets of encoded data slices are stored in a fixed sub-set of the “p” number of storage units.
 11. The DS module of claim 9 further comprises: the first module, when operable within the computing device, further causes the computing device to: ascertain the dispersed storage error encoding parameters and the “p” number of storage units such that “n” equals “p”; and ascertain the storage mapping such that, from set to set, a varying sub-set of the “n” number of encoded data slices of the plurality of sets of encoded data slices are stored in a fixed sub-set of the “p” number of storage units.
 12. The DS module of claim 9 further comprises: the first module, when operable within the computing device, further causes the computing device to: ascertain the dispersed storage error encoding parameters and the “p” number of storage units such that “n” equals “p”; and ascertain the storage mapping such that, from set to set, a fixed sub-set of the “n” number of encoded data slices of the plurality of sets of encoded data slices are stored in a varying sub-set of the “p” number of storage units.
 13. The DS module of claim 9 further comprises: the first module, when operable within the computing device, further causes the computing device to: ascertain the dispersed storage error encoding parameters and the “p” number of storage units such that “n” equals “p”; and ascertain the storage mapping such that, from set to set, a varying sub-set of the “n” number of encoded data slices of the plurality of sets of encoded data slices are stored in a varying sub-set of the “p” number of storage units.
 14. The DS module of claim 9 further comprises: the first module, when operable within the computing device, further causes the computing device to: ascertain the dispersed storage error encoding parameters and the “p” number of storage units such that “n” is less than “p”; and ascertain the storage mapping such that, from set to set, the “n” number of encoded data slices of the plurality of sets of encoded data slices are stored in a fixed sub-set of the “p” number of storage units.
 15. The DS module of claim 9 further comprises: the first module, when operable within the computing device, further causes the computing device to: ascertain the dispersed storage error encoding parameters and the “p” number of storage units such that “n” is less than “p”; and ascertain the storage mapping such that, from set to set, the “n” number of encoded data slices of the plurality of sets of encoded data slices are stored in a varying sub-set of the “p” number of storage units.
 16. The DS module of claim 9, wherein the data comprises one of: a data object; a data file; a plurality of data objects; a plurality of data files; a data segment of the data object; a data segment of the data file; a group of data segments of the data object; and a group of data segments of the data file. 